*! ivreg28 2.1.22 6July2007
*! authors cfb & mes
*! cloned from official ivreg version 5.0.9 19Dec2001
*! see end of file for version comments
* Variable naming:
* lhs = LHS endogenous
* endo = RHS endogenous (instrumented)
* inexog = included exogenous (instruments)
* exexog = excluded exogenous (instruments)
* iv = {inexog exexog} = all instruments
* rhs = {endo inexog} = RHS regressors
* 1 at the end of the name means the varlist after duplicates and collinearities removed
* ..1_ct at the end means a straight count of the list
* .._ct at the end means ..1_ct with any additional detected cnts removed
program define ivreg28, eclass byable(recall) sortpreserve
version 8.2
local lversion 02.1.22
local ivreg2_cmd "ivreg28"
if replay() {
syntax [, FIRST FFIRST RF Level(integer $S_level) NOHEader NOFOoter dropfirst droprf /*
*/ EForm(string) PLUS VERsion]
if "`version'" != "" & "`first'`ffirst'`rf'`noheader'`nofooter'`dropfirst'`droprf'`eform'`plus'" != "" {
di as err "option version not allowed"
error 198
}
if "`version'" != "" {
di in gr "`lversion'"
ereturn clear
ereturn local version `lversion'
exit
}
if `"`e(cmd)'"' != "`ivreg2_cmd'" {
error 301
}
if "`e(firsteqs)'" != "" & "`dropfirst'" == "" {
* On replay, set flag so saved eqns aren't dropped
local savefirst "savefirst"
}
if "`e(rfeq)'" != "" & "`droprf'" == "" {
* On replay, set flag so saved eqns aren't dropped
local saverf "saverf"
}
}
else {
syntax [anything(name=0)] [if] [in] [aw fw pw iw/] [, /*
*/ FIRST FFIRST NOID NOCOLLIN SAVEFIRST SAVEFPrefix(name) SMall Robust CLuster(varname) /*
*/ GMM CUE CUEINIT(string) CUEOPTions(string) ORTHOG(string) ENDOGtest(string) FWL(string) /*
*/ NOConstant Level(integer $S_level) Beta hc2 hc3 /*
*/ NOHEader NOFOoter NOOUTput title(string) subtitle(string) /*
*/ DEPname(string) EForm(string) PLUS /*
*/ BW(string) kernel(string) Tvar(varname) Ivar(varname)/*
*/ LIML COVIV FULLER(real 0) Kclass(string) /*
*/ REDundant(string) RF SAVERF SAVERFPrefix(name) /*
*/ B0(string) SMATRIX(string) WMATRIX(string) EWMATRIX(string) sw swpsd dofminus(integer 0) ]
local n 0
gettoken lhs 0 : 0, parse(" ,[") match(paren)
IsStop `lhs'
if `s(stop)' {
error 198
}
while `s(stop)'==0 {
if "`paren'"=="(" {
local n = `n' + 1
if `n'>1 {
capture noi error 198
di in red `"syntax is "(all instrumented variables = instrument variables)""'
exit 198
}
gettoken p lhs : lhs, parse(" =")
while "`p'"!="=" {
if "`p'"=="" {
capture noi error 198
di in red `"syntax is "(all instrumented variables = instrument variables)""'
di in red `"the equal sign "=" is required"'
exit 198
}
local endo `endo' `p'
gettoken p lhs : lhs, parse(" =")
}
* To enable Cragg HOLS estimator, allow for empty endo list
local temp_ct : word count `endo'
if `temp_ct' > 0 {
tsunab endo : `endo'
}
* To enable OLS estimator with (=) syntax, allow for empty exexog list
local temp_ct : word count `lhs'
if `temp_ct' > 0 {
tsunab exexog : `lhs'
}
}
else {
local inexog `inexog' `lhs'
}
gettoken lhs 0 : 0, parse(" ,[") match(paren)
IsStop `lhs'
}
local 0 `"`lhs' `0'"'
tsunab inexog : `inexog'
tokenize `inexog'
local lhs "`1'"
local 1 " "
local inexog `*'
if "`gmm'`cue'" != "" & "`exexog'" == "" {
di in red "option `gmm'`cue' invalid: no excluded instruments specified"
exit 102
}
* Process options
* Fuller implies LIML
if "`liml'" == "" & `fuller' != 0 {
local liml "liml"
}
* b0 implies nooutput and noid
if "`b0'" ~= "" {
local nooutput "nooutput"
local noid "noid"
}
if "`gmm'" != "" & "`cue'" != "" {
di as err "incompatible options: 2-step efficient gmm and cue gmm"
exit 198
}
if "`b0'" != "" & "`wmatrix'" != "" {
di as err "incompatible options: -b0- and -wmatrix-"
exit 198
}
if "`ewmatrix'" != "" {
if "`gmm'" != "" {
di as err "incompatible options: -ewmatrix- and 2-step efficient gmm"
exit 198
}
local wmatrix "`ewmatrix'"
}
* savefprefix implies savefirst
if "`savefprefix'" != "" & "`savefirst'" == "" {
local savefirst "savefirst"
}
* default savefprefix is _ivreg2_
if "`savefprefix'" == "" {
local savefprefix "_`ivreg2_cmd'_"
}
* saverfprefix implies saverf
if "`saverfprefix'" != "" & "`saverf'" == "" {
local saverf "saverf"
}
* default saverfprefix is _ivreg2_
if "`saverfprefix'" == "" {
local saverfprefix "_`ivreg2_cmd'_"
}
* LIML/kclass incompatibilities
if "`liml'`kclass'" != "" {
if "`gmm'`cue'" != "" {
di as err "GMM estimation not available with LIML or k-class estimators"
exit 198
}
if `fuller' < 0 {
di as err "invalid Fuller option"
exit 198
}
if "`liml'" != "" & "`kclass'" != "" {
di as err "cannot use liml and kclass options together"
exit 198
}
* Process kclass string
tempname kclass2
scalar `kclass2'=real("`kclass'")
if "`kclass'" != "" & (`kclass2' == . | `kclass2' < 0 ) {
di as err "invalid k-class option"
exit 198
}
}
* HAC estimation.
* If bw is omitted, default `bw' is empty string.
* If bw or kernel supplied, check/set `kernel'.
* Macro `kernel' is also used for indicating HAC in use.
if "`bw'" != "" | "`kernel'" != "" {
* Need tvar only for markout with time-series stuff
* but data must be tsset for time-series operators in code to work
if "`tvar'" == "" {
local tvar "`_dta[_TStvar]'"
}
else if "`tvar'"!="`_dta[_TStvar]'" {
di as err "invalid tvar() option - data already tsset"
exit 5
}
if "`ivar'" == "" {
local ivar "`_dta[_TSpanel]'"
}
else if "`ivar'"!="`_dta[_TSpanel]'" {
di as err "invalid ivar() option - data already tsset"
exit 5
}
if "`tvar'" == "" & "`ivar'" != "" {
di as err "missing tvar() option with ivar() option"
exit 5
}
if "`ivar'`tvar'"=="" {
capture tsset
}
else {
capture tsset `ivar' `tvar'
}
capture local tvar "`r(timevar)'"
capture local ivar "`r(panelvar)'"
if "`tvar'" == "" {
di as err "must tsset data and specify timevar"
exit 5
}
tsreport if `tvar' != .
if `r(N_gaps)' != 0 & "`ivar'"=="" {
di in gr "Warning: time variable " in ye "`tvar'" in gr " has " /*
*/ in ye "`r(N_gaps)'" in gr " gap(s) in relevant range"
}
if "`bw'" == "" {
di as err "bandwidth option bw() required for HAC-robust estimation"
exit 102
}
local bw real("`bw'")
* Check it's a valid bandwidth
if `bw' != int(`bw') | /*
*/ `bw' == . | /*
*/ `bw' <= 0 {
di as err "invalid bandwidth in option bw() - must be integer > 0"
exit 198
}
* Convert bw macro to simple integer
local bw=`bw'
* Check it's a valid kernel
local validkernel 0
if lower(substr("`kernel'", 1, 3)) == "bar" | "`kernel'" == "" {
* Default kernel
local kernel "Bartlett"
local window "lag"
local validkernel 1
if `bw'==1 {
di in ye "Note: kernel=Bartlett and bw=1 implies zero lags used. Standard errors and"
di in ye " test statistics are not autocorrelation-consistent."
}
}
if lower(substr("`kernel'", 1, 3)) == "par" {
local kernel "Parzen"
local window "lag"
local validkernel 1
if `bw'==1 {
di in ye "Note: kernel=Parzen and bw=1 implies zero lags used. Standard errors and"
di in ye " test statistics are not autocorrelation-consistent."
}
}
if lower(substr("`kernel'", 1, 3)) == "tru" {
local kernel "Truncated"
local window "lag"
local validkernel 1
}
if lower(substr("`kernel'", 1, 9)) == "tukey-han" | lower("`kernel'") == "thann" {
local kernel "Tukey-Hanning"
local window "lag"
local validkernel 1
if `bw'==1 {
di in ye "Note: kernel=Tukey-Hanning and bw=1 implies zero lags. Standard errors and"
di in ye " test statistics are not autocorrelation-consistent."
}
}
if lower(substr("`kernel'", 1, 9)) == "tukey-ham" | lower("`kernel'") == "thamm" {
local kernel "Tukey-Hamming"
local window "lag"
local validkernel 1
if `bw'==1 {
di in ye "Note: kernel=Tukey-Hamming and bw=1 implies zero lags. Standard errors and"
di in ye " test statistics are not autocorrelation-consistent."
}
}
if lower(substr("`kernel'", 1, 3)) == "qua" | lower("`kernel'") == "qs" {
local kernel "Quadratic spectral"
local window "spectral"
local validkernel 1
}
if lower(substr("`kernel'", 1, 3)) == "dan" {
local kernel "Daniell"
local window "spectral"
local validkernel 1
}
if lower(substr("`kernel'", 1, 3)) == "ten" {
local kernel "Tent"
local window "spectral"
local validkernel 1
}
if ~`validkernel' {
di in red "invalid kernel"
exit 198
}
}
if "`kernel'" != "" & "`cluster'" != "" {
di as err "cannot use HAC kernel estimator with -cluster- option"
exit 198
}
* changed below from `endog' to `endogtest' 2Aug06 MES
if "`orthog'`endogtest'`redundant'`fwl'" != "" {
capture tsunab orthog : `orthog'
capture tsunab endogtest : `endogtest'
capture tsunab redundant : `redundant'
capture tsunab fwl : `fwl'
}
if "`hc2'`hc3'" != "" {
if "`hc2'"!="" {
di in red "option `hc2' invalid"
}
else di in red "option `hc3' invalid"
exit 198
}
if "`beta'" != "" {
di in red "option `beta' invalid"
exit 198
}
* Weights
* fweight and aweight accepted as is
* iweight not allowed with robust or gmm and requires a trap below when used with summarize
* pweight is equivalent to aweight + robust
* but in HAC case, robust implied by `kernel' rather than `robust'
tempvar wvar
if "`weight'" == "fweight" | "`weight'"=="aweight" {
local wtexp `"[`weight'=`exp']"'
gen double `wvar'=`exp'
}
if "`weight'" == "fweight" & "`kernel'" !="" {
di in red "fweights not allowed (data are -tsset-)"
exit 101
}
if "`weight'" == "iweight" {
if "`robust'`cluster'`gmm'`kernel'" !="" {
di in red "iweights not allowed with robust or gmm"
exit 101
}
else {
local wtexp `"[`weight'=`exp']"'
gen double `wvar'=`exp'
}
}
if "`weight'" == "pweight" {
local wtexp `"[aweight=`exp']"'
gen double `wvar'=`exp'
local robust "robust"
}
if "`weight'" == "" {
* If no weights, define neutral weight variable
qui gen byte `wvar'=1
}
* If no kernel (=no HAC) then gmm implies (heteroskedastic-) robust
if "`kernel'" == "" & "`gmm'" != "" {
local robust "robust"
}
if `dofminus' > 0 {
local dofmopt "dofminus(`dofminus')"
}
* Stock-Watson robust SEs.
if "`sw'`swpsd'" ~= "" {
if "`kernel'" ~= "" {
di as err "Stock-Watson robust SEs not supported with -kernel- option"
exit 198
}
if "`cue'" ~= "" {
di as err "Stock-Watson robust SEs not supported with -cue- option"
exit 198
}
if "`ivar'"=="" {
di as err "Must specify -ivar- with -sw- option"
exit 198
}
}
marksample touse
markout `touse' `lhs' `inexog' `exexog' `endo' `cluster' `tvar', strok
* Weight statement
if "`weight'" ~= "" {
sum `wvar' if `touse' `wtexp', meanonly
di in gr "(sum of wgt is " %14.4e `r(sum_w)' ")"
}
* Set local macro T and check that bw < T
* Also make sure only used sample is checked
if "`bw'" != "" {
sum `tvar' if `touse', meanonly
local T = r(max)-r(min)+1
if `bw' > `T' {
di as err "invalid bandwidth in option bw() - cannot exceed timespan of data"
exit 198
}
}
************* Collinearities and duplicates block *****************
if "`noconstant'" != "" {
local rmcnocons "nocons"
}
* Check for duplicates of variables
* To mimic official ivreg, in the case of duplicates,
* (1) inexog > endo
* (2) inexog > exexog
* (3) endo + exexog = inexog, as if it were "perfectly predicted"
local dupsen1 : list dups endo
local endo1 : list uniq endo
local dupsex1 : list dups exexog
local exexog1 : list uniq exexog
local dupsin1 : list dups inexog
local inexog1 : list uniq inexog
* Remove inexog from endo
local dupsen2 : list endo1 & inexog1
local endo1 : list endo1 - inexog1
* Remove inexog from exexog
local dupsex2 : list exexog1 & inexog1
local exexog1 : list exexog1 - inexog1
* Remove endo from exexog
local dupsex3 : list exexog1 & endo1
local exexog1 : list exexog1 - endo1
local dups "`dupsen1' `dupsex1' `dupsin1' `dupsen2' `dupsex2' `dupsex3'"
local dups : list uniq dups
if "`nocollin'" == "" {
* First, collinearities check using canonical correlations approach
* Eigenvalue=1 => included endog is really included exogenous
* Eigenvalue=0 => included endog collinear with another included endog
* Corresponding column names give name of variable
* Code block stolen from below, so some repetition
local insts1 `inexog1' `exexog1'
local rhs1 `endo1' `inexog1'
local iv1_ct : word count `insts1'
local rhs1_ct : word count `rhs1'
local endo1_ct : word count `endo1'
local exex1_ct : word count `exexog1'
local endoexex1_ct : word count `endo1' `exexog1'
local inexog1_ct : word count `inexog1'
if `endo1_ct' > 0 {
tempname ccmat ccrealev ccimagev cc A XX XXinv ZZ ZZinv XZ XPZX
qui mat accum `A' = `endo1' `insts1' if `touse' `wtexp', `rmcnocons'
mat `XX' = `A'[1..`endo1_ct',1..`endo1_ct']
mat `XXinv'=syminv(`XX')
mat `ZZ' = `A'[`endo1_ct'+1...,`endo1_ct'+1...]
mat `ZZinv'=syminv(`ZZ')
mat `XZ' = `A'[1..`endo1_ct',`endo1_ct'+1...]
mat `XPZX'=`XZ'*`ZZinv'*`XZ''
mat `ccmat' = `XXinv'*`XPZX'
mat eigenvalues `ccrealev' `ccimagev' = `ccmat'
foreach vn of varlist `endo1' {
local i=colnumb(`ccmat',"`vn'")
if round(`ccmat'[`i',`i'],10e-7)==0 {
* Collinear with another endog, so remove from endog list
local endo1 : list endo1-vn
}
if round(`ccmat'[`i',`i'],10e-7)==1 {
* Collinear with exogenous, so remove from endog and add to inexog
local endo1 : list endo1-vn
local inexog1 "`inexog1' `vn'"
local ecollin "`ecollin' `vn'"
}
}
* Loop through endo1 to find Eigenvalues=0 or 1
}
* Remove collinearities. Use _rmcollright to enforce same priority as above.
capture version 9.2
if _rc==0 {
* _rmcollright crashes if no arguments supplied
capture _rmcollright `inexog1' `exexog1' if `touse' `wtexp', `rmcnocons'
}
else {
qui _rmcoll `inexog1' `exexog1' if `touse' `wtexp', `rmcnocons'
}
version 8.2
* endo1 has had within-endo collinear removed, so non-colllinear list is _rmcoll result + endo1
local ncvars `r(varlist)' `endo1'
local allvars1 `endo1' `inexog1' `exexog1'
* collin gets collinear variables to be removed
local collin : list allvars1-ncvars
* Remove collin from exexog1
local exexog1 : list exexog1-collin
* Remove collin from inexog1
local inexog1 : list inexog1-collin
* Collinearity and duplicates warning messages, if necessary
if "`dups'" != "" {
di in gr "Warning - duplicate variables detected"
di in gr "Duplicates:" _c
Disp `dups', _col(21)
}
if "`ecollin'" != "" {
di in gr "Warning - endogenous variable(s) collinear with instruments"
di in gr "Vars now exogenous:" _c
Disp `ecollin', _col(21)
}
if "`collin'" != "" {
di in gr "Warning - collinearities detected"
di in gr "Vars dropped:" _c
Disp `collin', _col(21)
}
}
**** End of collinearities block ************
**** Partial-out FWL block ******************
if "`fwl'" != "" {
preserve
local fwl : subinstr local fwl "_cons" "", all count(local fwlcons)
if `fwlcons' > 0 & "`noconstant'"~="" {
di in r "Error: _cons listed in fwl() but equation specifies -noconstant-."
error 198
}
else if "`noconstant'"~="" {
local fwlcons 0
}
else {
* Just in case of multiple _cons
local fwlcons 1
}
local fwldrop : list inexog - inexog1
local fwl1 : list fwl - fwldrop
local fwlcheck : list fwl1 - inexog1
if ("`fwlcheck'"~="") {
di in r "Error: `fwlcheck' listed in fwl() but not in list of regressors."
error 198
}
local inexog1 : list inexog1 - fwl1
if "`cluster'"~="" {
* Check that cluster var won't be transformed
local allvars "`lhs' `inexog' `endo' `exexog'"
local clustvarcheck : list cluster in allvars
if `clustvarcheck' {
di in r "Error: cannot use cluster variable `cluster' as dependent variable, regressor or IV"
di in r " in combination with -fwl- option."
error 198
}
}
* Constant is partialled out, unless nocons already specified in the first place
tempname fwl_resid
foreach var of varlist `lhs' `inexog1' `endo1' `exexog1' {
qui regress `var' `fwl1' if `touse' `wtexp', `noconstant'
qui predict double `fwl_resid' if `touse', resid
qui replace `var' = `fwl_resid'
drop `fwl_resid'
}
local fwl_ct : word count `fwl1'
if "`noconstant'" == "" {
* fwl_ct used for small-sample adjustment to regression F-stat
local fwl_ct = `fwl_ct' + 1
local noconstant "noconstant"
}
}
else {
* Set count of fwl vars to zero if option not used
local fwl_ct 0
}
*********************************************
local insts1 `inexog1' `exexog1'
local rhs1 `endo1' `inexog1'
local iv1_ct : word count `insts1'
local rhs1_ct : word count `rhs1'
local endo1_ct : word count `endo1'
local exex1_ct : word count `exexog1'
local endoexex1_ct : word count `endo1' `exexog1'
local inexog1_ct : word count `inexog1'
if "`noconstant'" == "" {
local cons_ct 1
}
else {
local cons_ct 0
}
if `rhs1_ct' > `iv1_ct' {
di in red "equation not identified; must have at " /*
*/ "least as many instruments not in"
di in red "the regression as there are " /*
*/ "instrumented variables"
exit 481
}
if `rhs1_ct' + `cons_ct' == 0 {
di in red "error: no regressors specified"
exit 102
}
if "`cluster'"!="" {
local clopt "cluster(`cluster')"
if "`robust'"=="" {
local robust "robust"
}
}
if "`bw'"!="" {
local bwopt "bw(`bw')"
}
if "`kernel'"!="" {
local kernopt "kernel(`kernel')"
}
* If depname not provided (default) name is lhs variable
if "`depname'"=="" {
local depname `lhs'
}
************************************************************************************************
* Cross-products and basic IV coeffs, residuals and moment conditions
tempvar iota y2 yhat ivresid ivresid2 gresid gresid2 lresid lresid2 b0resid b0resid2 s1resid
tempname Nprec ysum yy yyc r2u r2c B V ivB gmmB wB lB gmmV ivest
tempname r2 r2_a ivrss lrss wbrss b0rss rss mss rmse sigmasq iv_s2 l_s2 wb_s2 b0_s2 F Fp Fdf2
tempname S Sinv W s1Zu s2Zu b0Zu wbZu wbresid wbresid2 s1sigmasq
tempname A XZ XZa XZb Zy ZZ ZZinv XPZX XPZXinv XPZy
tempname YY Z2Z2 ZY Z2Y XXa XXb XX Xy Z2Z2inv XXinv
tempname XZWZX XZWZXinv XZWZy XZW
tempname B V B1 uZSinvZu j jp arubin arubinp tempmat
* Generate cross-products of y, X, Z
qui matrix accum `A' = `lhs' `endo1' `exexog1' `inexog1' /*
*/ if `touse' `wtexp', `noconstant'
if "`noconstant'"=="" {
matrix rownames `A' = `lhs' `endo1' `exexog1' /*
*/ `inexog1' _cons
matrix colnames `A' = `lhs' `endo1' `exexog1' /*
*/ `inexog1' _cons
}
else {
matrix rownames `A' = `lhs' `endo1' `exexog1' `inexog1'
matrix colnames `A' = `lhs' `endo1' `exexog1' `inexog1'
}
if `endo1_ct' > 0 {
* X'Z is [endo1 inexog1]'[exexog1 inexog1]
mat `XZ'=`A'[2..`endo1_ct'+1,`endo1_ct'+2...]
* Append portion corresponding to included exog if they (incl constant) exist
if 2+`endo1_ct'+`iv1_ct'-(`rhs1_ct'-`endo1_ct') /*
*/ <= rowsof(`A') {
mat `XZ'=`XZ' \ /*
*/ `A'[2+`endo1_ct'+`iv1_ct'- /*
*/ (`rhs1_ct'-`endo1_ct')..., /*
*/ `endo1_ct'+2...]
}
* If included exog (incl const) exist, create XX matrix in 3 steps
if `inexog1_ct' + `cons_ct' > 0 {
mat `XXa' = `A'[2..`endo1_ct'+1, 2..`endo1_ct'+1], /*
*/ `A'[2..`endo1_ct'+1, `endoexex1_ct'+2...]
mat `XXb' = `A'[`endoexex1_ct'+2..., 2..`endo1_ct'+1], /*
*/ `A'[`endoexex1_ct'+2..., `endoexex1_ct'+2...]
mat `XX' = `XXa' \ `XXb'
mat `Xy' = `A'[2..`endo1_ct'+1, 1] \ `A'[`endoexex1_ct'+2..., 1]
}
else {
mat `XX' = `A'[2..`endo1_ct'+1, 2..`endo1_ct'+1]
mat `Xy' = `A'[2..`endo1_ct'+1, 1]
}
}
else {
* Cragg HOLS estimator with no endogenous variables
mat `XZ'= `A'[2+`iv1_ct'-(`rhs1_ct'-`endo1_ct')..., /*
*/ 2...]
mat `XX' = `A'[`endoexex1_ct'+2..., `endoexex1_ct'+2...]
mat `Xy' = `A'[`endoexex1_ct'+2..., 1]
}
mat `XX'=(`XX'+`XX'')/2
mat `XXinv'=syminv(`XX')
mat `Zy'=`A'[`endo1_ct'+2...,1]
mat `ZZ'=`A'[`endo1_ct'+2...,`endo1_ct'+2...]
mat `ZZ'=(`ZZ'+`ZZ'')/2
mat `ZZinv'=syminv(`ZZ')
* diag0cnt probably superfluous since collinearity checks will catch this unless disabled
local iv_ct = rowsof(`ZZ') - diag0cnt(`ZZinv')
mat `YY'=`A'[1..`endo1_ct'+1, 1..`endo1_ct'+1]
mat `ZY' = `A'[`endo1_ct'+2..., 1..`endo1_ct'+1]
mat `XPZX'=`XZ'*`ZZinv'*`XZ''
mat `XPZX'=(`XPZX'+`XPZX'')/2
mat `XPZXinv'=syminv(`XPZX')
mat `XPZy'=`XZ'*`ZZinv'*`Zy'
******************************
qui gen byte `iota'=1
qui gen double `y2'=`lhs'^2
* Stata summarize won't work with iweights, so must use matrix cross-product
qui matrix vecaccum `ysum' = `iota' `y2' `lhs' `wtexp' if `touse'
* Nprec is ob count from mat accum. Use this rather than `N' in calculations
* here and below because in official -regress- `N' is rounded if iweights are used.
scalar `Nprec'=`ysum'[1,3]
if "`weight'" == "iweight" {
scalar `Nprec'=round(`Nprec')
}
local N=round(`Nprec')
scalar `yy'=`ysum'[1,1]
scalar `yyc'=`yy'-`ysum'[1,2]^2/`Nprec'
*******************************************************************************************
* First-step estimators: b0, wmatrix, LIML-kclass, IV.
* Generate residuals s1resid for used in 2SFEGMM and robust.
* User-supplied b0 provides value of CUE obj fn.
if "`b0'" != "" {
capture drop `yhat'
qui mat score double `yhat' = `b0' if `touse'
qui gen double `b0resid'=`lhs'-`yhat'
qui gen double `b0resid2'=`b0resid'^2
capture drop `ysum'
qui matrix vecaccum `ysum' = `iota' `b0resid2' /*
*/ `wtexp' if `touse', `noconstant'
scalar `b0rss'= `ysum'[1,1]
* Adjust sigma-squared for dofminus
scalar `b0_s2'=`b0rss'/(`Nprec'-`dofminus')
scalar `s1sigmasq'=`b0_s2'
qui gen double `s1resid'=`b0resid'
}
else if "`wmatrix'" != "" {
* GMM with arbitrary weighting matrix provides first-step estimates
local cn : colnames(`ZZ')
matrix `W'=`wmatrix'
* Rearrange/select columns to mat IV matrix
capture matsort `W' "`cn'"
local wrows = rowsof(`W')
local wcols = colsof(`W')
local zcols = colsof(`ZZ')
if _rc ~= 0 | (`wrows'~=`zcols') | (`wcols'~=`zcols') {
di as err "-wmatrix- option error: supplied matrix columns/rows do not match IV list"
exit 198
}
mat `XZWZX'=`XZ'*`W'*`XZ''
mat `XZWZy'=`XZ'*`W'*`Zy'
mat `XZWZX'=(`XZWZX'+`XZWZX'')/2
mat `XZWZXinv'=syminv(`XZWZX')
mat `XZW'=`XZ'*`W'
mat `wB'=`XZWZy''*`XZWZXinv''
capture drop `yhat'
qui mat score double `yhat' = `wB' if `touse'
qui gen double `wbresid'=`lhs'-`yhat'
qui gen double `wbresid2'=`wbresid'^2
capture drop `ysum'
qui matrix vecaccum `ysum' = `iota' `wbresid2' /*
*/ `wtexp' if `touse', `noconstant'
scalar `wbrss'= `ysum'[1,1]
* Adjust sigma-squared for dofminus
scalar `wb_s2'=`wbrss'/(`Nprec'-`dofminus')
scalar `s1sigmasq'=`wb_s2'
qui gen double `s1resid'=`wbresid'
}
else if "`liml'`kclass'" != "" {
* LIML and kclass code
tempname WW WW1 Evec Eval Evaldiag target lambda lambda2 khs XhXh XhXhinv ll
if "`kclass'" == "" {
* LIML block
matrix `WW' = `YY' - `ZY''*`ZZinv'*`ZY'
if `inexog1_ct' + `cons_ct' > 0 {
mat `Z2Y' = `A'[`endoexex1_ct'+2..., 1..`endo1_ct'+1]
mat `Z2Z2' = `A'[`endoexex1_ct'+2..., `endoexex1_ct'+2...]
mat `Z2Z2'=(`Z2Z2'+`Z2Z2'')/2
mat `Z2Z2inv' = syminv(`Z2Z2')
matrix `WW1' = `YY' - `Z2Y''*`Z2Z2inv'*`Z2Y'
}
else {
* Special case of no included exogenous (incl constant)
matrix `WW1' = `YY'
}
matrix `WW'=(`WW'+`WW'')/2
matrix symeigen `Evec' `Eval' = `WW'
matrix `Evaldiag' = diag(`Eval')
* Replace diagonal elements of Evaldiag with the element raised to the power (-1/2)
local i 1
while `i' <= rowsof(`Evaldiag') {
* Need to use capture because with collinearities, diag may be virtually zero
* ... but actually negative
capture matrix `Evaldiag'[`i',`i'] = /*
*/ `Evaldiag'[`i',`i']^(-0.5)
local i = `i'+1
}
matrix `target' = (`Evec'*`Evaldiag'*`Evec'') * `WW1' /*
*/ * (`Evec'*`Evaldiag'*`Evec'')
* Re-use macro names
matrix `target'=(`target'+`target'')/2
matrix symeigen `Evec' `Eval' = `target'
* Get smallest eigenvalue
* Note that collinearities can yield a nonsense eigenvalue appx = 0
* and just-identified will yield an eigenvalue that is ALMOST exactly = 1
* so require it to be >= 0.9999999999.
local i 1
scalar `lambda'=.
scalar `lambda2'=.
while `i' <= colsof(`Eval') {
if (`lambda' > `Eval'[1,`i']) & (`Eval'[1,`i'] >=0.9999999999) {
scalar `lambda2' = `lambda'
scalar `lambda' = `Eval'[1,`i']
}
local i = `i'+1
}
if `fuller'==0 {
* Basic LIML. Macro kclass2 is the scalar.
scalar `kclass2'=`lambda'
}
else {
* Fuller LIML
if `fuller' > (`N'-`iv_ct') {
di as err "error: invalid choice of Fuller LIML parameter"
exit 198
}
scalar `kclass2' = `lambda' - `fuller'/(`N'-`iv_ct')
}
* End of LIML block
}
mat `XhXh'=(1-`kclass2')*`XX'+`kclass2'*`XPZX'
mat `XhXh'=(`XhXh'+`XhXh'')/2
mat `XhXhinv'=syminv(`XhXh')
mat `lB'=`Xy''*`XhXhinv'*(1-`kclass2') + `kclass2'*`Zy''*`ZZinv'*`XZ''*`XhXhinv'
capture drop `yhat'
qui mat score double `yhat'=`lB' if `touse'
qui gen double `lresid'=`lhs' - `yhat'
qui gen double `lresid2'=`lresid'^2
capture drop `ysum'
qui matrix vecaccum `ysum' = `iota' `lresid2' /*
*/ `wtexp' if `touse', `noconstant'
scalar `lrss'= `ysum'[1,1]
* Adjust sigma-squared for dofminus
scalar `l_s2'=`lrss'/(`Nprec'-`dofminus')
scalar `s1sigmasq'=`l_s2'
qui gen double `s1resid'=`lresid'
}
else {
* IV resids are 1st-step GMM resids
* In these expressions, ignore scaling of W
mat `ivB' = `XPZy''*`XPZXinv''
mat `XZWZX'=`XPZX'
mat `XZWZXinv'=`XPZXinv'
mat `XZW'=`XZ'*`ZZinv'
capture drop `yhat'
qui mat score double `yhat' = `ivB' if `touse'
qui gen double `ivresid'=`lhs'-`yhat'
qui gen double `ivresid2'=`ivresid'^2
capture drop `ysum'
qui matrix vecaccum `ysum' = `iota' `ivresid2' /*
*/ `wtexp' if `touse', `noconstant'
scalar `ivrss'=`ysum'[1,1]
scalar `iv_s2'=`ivrss'/(`Nprec'-`dofminus')
scalar `s1sigmasq'=`iv_s2'
qui gen double `s1resid'=`ivresid'
}
* Orthogonality conditions using step 1 residuals
qui mat vecaccum `s1Zu'=`s1resid' `exexog1' `inexog1' /*
*/ `wtexp' if `touse', `noconstant'
*******************************************************************************************
* S covariance matrix of orthogonality conditions
*******************************************************************************************
* If user-supplied S matrix is used, use it
if "`smatrix'" != "" {
local cn : colnames(`ZZ')
matrix `S'=`smatrix'
* Rearrange/select columns to mat IV matrix
capture matsort `S' "`cn'"
local srows = rowsof(`S')
local scols = colsof(`S')
local zcols = colsof(`ZZ')
if _rc ~= 0 | (`srows'~=`zcols') | (`scols'~=`zcols') {
di as err "-smatrix- option error: supplied matrix columns/rows do not match IV list"
exit 198
}
mat `S' = (`S' + `S'') / 2
mat `Sinv'=syminv(`S')
local rankS = rowsof(`Sinv') - diag0cnt(`Sinv')
}
*******************************************************************************************
* Start robust block for robust-HAC S and Sinv
* Do not enter if user supplies smatrix or if CUE
if "`robust'`cluster'" != "" & "`cue'"=="" & "`smatrix'"=="" {
* Optimal weighting matrix
* Block calculates S_0 robust matrix
* _robust has same results as
* mat accum `S'=`exexog1' `inexog1' [iweight=`ivresid'^2] if `touse'
* mat `S' = `S'*1/`Nprec'
* _robust doesn't work properly with TS variables, so must first tsrevar
tsrevar `exexog1' `inexog1'
local TSinsts1 `r(varlist)'
* Create identity matrix with matching col/row names
mat `S'=I(colsof(`s1Zu'))
if "`noconstant'"=="" {
mat colnames `S' = `TSinsts1' "_cons"
mat rownames `S' = `TSinsts1' "_cons"
}
else {
mat colnames `S' = `TSinsts1'
mat rownames `S' = `TSinsts1'
}
_robust `s1resid' `wtexp' if `touse', variance(`S') `clopt' minus(0)
if "`cluster'"!="" {
local N_clust=r(N_clust)
}
mat `S' = `S'*1/`Nprec'
* Above doesn't work properly with iweights (i.e. yield same matrix as fw),
* hence iweight trap at start
if "`kernel'" != "" {
* HAC block for S_1 onwards matrices
tempvar vt1
qui gen double `vt1' = .
tempname tt tx kw karg ow
* Use insts with TS ops removed and with iota (constant) column
if "`noconstant'"=="" {
local insts1c "`TSinsts1' `iota'"
}
else {
local insts1c "`TSinsts1'"
}
local iv1c_ct : word count `insts1c'
* "tau=0 loop" is S_0 block above for all robust code
local tau 1
* Spectral windows require looping through all T-1 autocovariances
if "`window'" == "spectral" {
local TAU `T'-1
di in ye "Computing kernel ..."
}
else {
local TAU `bw'
}
if "`weight'" == "" {
* If no weights specified, define neutral ow variable and weight expression for code below
qui gen byte `ow'=1
local wtexp `"[fweight=`wvar']"'
}
else {
* pweights and aweights
summ `wvar' if `touse', meanonly
qui gen double `ow' = `wvar'/r(mean)
}
while `tau' <= `TAU' {
capture mat drop `tt'
local i 1
while `i' <= `iv1c_ct' {
local x : word `i' of `insts1c'
* Add lags defined with TS operators
local Lx "L`tau'.`x'"
local Ls1resid "L`tau'.`s1resid'"
local Low "L`tau'.`ow'"
qui replace `vt1' = `Lx'*`s1resid'* /*
*/ `Ls1resid'*`Low'*`ow' if `touse'
* Use capture here because there may be insufficient observations, e.g., if
* the IVs include lags and tau=N-1. _rc will be 2000 in this case.
capture mat vecaccum `tx' = `vt1' `insts1c' /*
*/ if `touse', nocons
if _rc == 0 {
mat `tt' = nullmat(`tt') \ `tx'
}
local i = `i'+1
}
* bw = bandwidth, karg is argument to kernel function, kw is kernel function (weight)
scalar `karg' = `tau'/(`bw')
if "`kernel'" == "Truncated" {
scalar `kw'=1
}
if "`kernel'" == "Bartlett" {
scalar `kw'=(1-`karg')
}
if "`kernel'" == "Parzen" {
if `karg' <= 0.5 {
scalar `kw' = 1-6*`karg'^2+6*`karg'^3
}
else {
scalar `kw' = 2*(1-`karg')^3
}
}
if "`kernel'" == "Tukey-Hanning" {
scalar `kw'=0.5+0.5*cos(_pi*`karg')
}
if "`kernel'" == "Tukey-Hamming" {
scalar `kw'=0.54+0.46*cos(_pi*`karg')
}
if "`kernel'" == "Tent" {
scalar `kw'=2*(1-cos(`tau'*`karg')) / (`karg'^2)
}
if "`kernel'" == "Daniell" {
scalar `kw'=sin(_pi*`karg') / (_pi*`karg')
}
if "`kernel'" == "Quadratic spectral" {
scalar `kw'=25/(12*_pi^2*`karg'^2) /*
*/ * ( sin(6*_pi*`karg'/5)/(6*_pi*`karg'/5) /*
*/ - cos(6*_pi*`karg'/5) )
}
* Need -capture-s here because tt may not exist (because of insufficient observations/lags)
capture mat `tt' = (`tt'+`tt'')*`kw'*1/`Nprec'
if _rc == 0 {
mat `S' = `S' + `tt'
}
local tau = `tau'+1
}
if "`weight'" == "" {
* If no weights specified, remove neutral weight variables
local wtexp ""
}
}
* To give S the right col/row names
mat `S'=`S'+0*diag(`s1Zu')
* Right approach is to adjust S by N/(N-dofminus) if NOT cluster
* because clustered S is already "adjusted"
if "`cluster'"=="" {
mat `S'=`S'*`Nprec'/(`Nprec'-`dofminus')
}
* Stock-Watson robust SEs. Requires `wvar' to be defined above.
* Correspondence between S-W (2006) and code below assumes ivreg2 is called on demeaned data.
* Variable ivar identifies the observational unit.
* wvar will simply be 1 for all observations unless weights are used.
* T_i is number of observations for an observational unit, extended to unbalanced data.
* SW consider only balanced data and denote this as T (constant acros units).
* s1resid is (fixed effects) residuals. SW denote this as u_tilda_hat (p. 2, eqn 4).
* s2 is, for an observational unit i, 1/(T-1) * sum of squared (fixed effects) residuals.
* This is the second term in () in the expression for B_hat in SW eqn 6, p. 3.
* mat opaccum calculates a cross-prod of the form A = X1'e1e1'X1 + X2'e2e2'X2 + ... + Xk'ekek'Xk
* ei is s from above. eiei' is a T_i x T_i matrix filled with s_i2s. Thus the cross-prod becomes
* A = s_1^2*X1'X1 + s_2^2*X2'e2e2'X2 + ... + s_k^2*Xk'Xk
* which is the form of B_hat in SW eqn 6 p. 3, except for the missing 1/N and 1/T
* In unbalanced case, 1/T isn't constant, so must incorporate the 1/T that weights the Xi'Xi into the s,
* hence the second division of s2 by T_i.
* S is SW's Sigma_hat_HR-FE, which is the fixed effects S (=Sigma_hat_HR-XS) minus 1/(T-1)*B)hat
* and then multiplied by (T-1)/(T-2). In SW, T is constant because they cover only the balanced case.
* Here, T varies across units, so we use the harmonic mean of T for T_bar.
* PSD code by CFB based on SW point 10 on p. 6. Guarantees S will be PSD.
if "`sw'`swpsd'" ~= "" {
tempname B s s2 T_i T_inv T_bar s1resid2
qui gen double `s1resid2'=`s1resid'^2
sort `ivar' `touse'
qui by `ivar' `touse': gen long `T_i' = sum(`wvar') if `touse'
qui by `ivar' `touse': replace `T_i' = `T_i'[_N] if `touse' & _n<_N
qui gen `T_inv' = 1/`T_i'
sum `T_inv' if `touse', meanonly
scalar `T_bar' = 1/r(mean)
qui by `ivar' `touse': gen double `s2'=sum(`s1resid2'*`wvar') if `touse'
qui by `ivar' `touse': replace `s2'=`s2'[_N] if `touse' & _n<_N
qui replace `s2' = `s2'/(`T_i'-1)
qui replace `s2' = `s2'/`T_i'
qui gen double `s' = sqrt(`s2')
qui mat opaccum `B'=`exexog1' `inexog1' `wtexp' if `touse', /*
*/ group(`ivar') opvar(`s') `noconstant'
mat `B' = `B' * 1/`Nprec'
mat `S' = (`T_bar'-1)/(`T_bar'-2)*(`S' - `B'*1/(`T_bar'-1))
if "`swpsd'" ~= "" {
mat `S'=(`S'+`S'')/2
tempname X v
mat symeigen `X' `v' = `S'
local ncol = colsof(`S')
forv i=1/`ncol' {
mat `v'[1,`i']= abs(`v'[1,`i'])
}
mat `S' = `X' * diag(`v') * `X''
}
}
mat `S'=(`S'+`S'')/2
mat `Sinv'=syminv(`S')
local rankS = rowsof(`Sinv') - diag0cnt(`Sinv')
}
* End robust-HAC S and Sinv block
************************************************************************************
* Block for non-robust S and Sinv, including autocorrelation-consistent (AC).
* Do not enter if user supplies smatrix or if cue
if "`robust'`cluster'`cue'`smatrix'"=="" {
* First do with S_0 (=S for simple IV)
* Step 1 sigma^2 is IV sigma^2 unless b0 or wmatrix provided
mat `S' = `s1sigmasq'*`ZZ'*(1/`Nprec')
if "`kernel'" != "" {
* AC code for S_1 onwards matrices
tempvar vt1
qui gen double `vt1' = .
tempname tt tx kw karg ow sigttj
* Use insts with TS ops removed and with iota (constant) column
tsrevar `exexog1' `inexog1'
local TSinsts1 `r(varlist)'
if "`noconstant'"=="" {
local insts1c "`TSinsts1' `iota'"
}
else {
local insts1c "`TSinsts1'"
}
local iv1c_ct : word count `insts1c'
* "tau=0 loop" is S_0 block above
local tau 1
* Spectral windows require looping through all T-1 autocovariances
if "`window'" == "spectral" {
local TAU `T'-1
di in ye "Computing kernel ..."
}
else {
local TAU `bw'
}
if "`weight'" == "" {
* If no weights specified, define neutral ow variable and wtexp for code below
qui gen byte `ow'=1
local wtexp `"[fweight=`wvar']"'
}
else {
* pweights and aweights
summ `wvar' if `touse', meanonly
qui gen double `ow' = `wvar'/r(mean)
}
while `tau' <= `TAU' {
capture mat drop `tt'
local i 1
* errflag signals problems that make this loop's tt invalid
local errflag 0
* Additional marksample/markout required so that treatment of MVs is consistent across all IVs
marksample touse2
markout `touse2' `insts1c' L`tau'.(`insts1c')
local Low "L`tau'.`ow'"
while `i' <= `iv1c_ct' {
local x : word `i' of `insts1c'
* Add lags defined with TS operators
local Lx "L`tau'.`x'"
qui replace `vt1'=.
qui replace `vt1' = `Lx'*`Low'*`ow' if `touse' & `touse2'
* Use capture here because there may be insufficient observations, e.g., if
* the IVs include lags and tau=N-1. _rc will be 2000 in this case.
capture mat vecaccum `tx' = `vt1' `insts1c' /*
*/ if `touse', nocons
if _rc == 0 {
mat `tt' = nullmat(`tt') \ `tx'
}
local i = `i'+1
}
capture mat `tt' = 1/`Nprec' * `tt'
if _rc != 0 {
local errflag = 1
}
local Ls1resid "L`tau'.`s1resid'"
* Weights belong here as well
tempvar ivLiv
qui gen double `ivLiv' = `s1resid'*`Ls1resid'*`ow'*`Low' if `touse'
qui sum `ivLiv' if `touse', meanonly
scalar `sigttj' = r(sum)/`Nprec'
capture mat `tt' = `sigttj' * `tt'
* bw = bandwidth, karg is argument to kernel function, kw is kernel function (weight)
scalar `karg' = `tau'/(`bw')
if "`kernel'" == "Truncated" {
scalar `kw'=1
}
if "`kernel'" == "Bartlett" {
scalar `kw'=(1-`karg')
}
if "`kernel'" == "Parzen" {
if `karg' <= 0.5 {
scalar `kw' = 1-6*`karg'^2+6*`karg'^3
}
else {
scalar `kw' = 2*(1-`karg')^3
}
}
if "`kernel'" == "Tukey-Hanning" {
scalar `kw'=0.5+0.5*cos(_pi*`karg')
}
if "`kernel'" == "Tukey-Hamming" {
scalar `kw'=0.54+0.46*cos(_pi*`karg')
}
if "`kernel'" == "Tent" {
scalar `kw'=2*(1-cos(`tau'*`karg')) / (`karg'^2)
}
if "`kernel'" == "Daniell" {
scalar `kw'=sin(_pi*`karg') / (_pi*`karg')
}
if "`kernel'" == "Quadratic spectral" {
scalar `kw'=25/(12*_pi^2*`karg'^2) /*
*/ * ( sin(6*_pi*`karg'/5)/(6*_pi*`karg'/5) /*
*/ - cos(6*_pi*`karg'/5) )
}
* Need -capture-s here because tt may not exist (because of insufficient observations/lags)
capture mat `tt' = (`tt'+`tt'')*`kw'
if _rc != 0 {
local errflag = 1
}
* Accumulate if tt is valid
if `errflag' == 0 {
capture mat `S' = `S' + `tt'
}
local tau = `tau'+1
}
if "`weight'" == "" {
* If no weights specified, remove neutral weight variables
local wtexp ""
}
}
* End of AC code
* To give S the right col/row names
mat `S'=`S'+0*diag(`s1Zu')
mat `S'=(`S'+`S'')/2
mat `Sinv'=syminv(`S')
local rankS = rowsof(`Sinv') - diag0cnt(`Sinv')
}
* End of non-robust S and Sinv code (including AC)
*******************************************************************************************
* 2nd step and final coefficients
*******************************************************************************************
* User-supplied b0. CUE objective function.
if "`b0'" ~= "" {
mat `B' = `b0'
scalar `rss'=`b0rss'
scalar `sigmasq'=`b0_s2'
mat `W' = `Sinv'
}
*******************************************************************************************
* Block for gmm 2nd step to get coefficients and 2nd step residuals
* Non-robust IV, LIML, k-class, CUE do not enter
if "`gmm'`robust'`cluster'`kernel'`wmatrix'" != "" & "`cue'"=="" & "`ewmatrix'"=="" {
mat `tempmat'=`XZ'*`Sinv'*`XZ''
mat `tempmat'=(`tempmat'+`tempmat'')/2
mat `B1'=syminv(`tempmat')
mat `B1'=(`B1'+`B1'')/2
mat `gmmB'=(`B1'*`XZ'*`Sinv'*`Zy')'
capture drop `yhat'
qui mat score double `yhat'=`gmmB' if `touse'
qui gen double `gresid'=`lhs'-`yhat'
qui gen double `gresid2'=`gresid'^2
qui mat vecaccum `s2Zu'=`gresid' `exexog1' `inexog1' /*
*/ `wtexp' if `touse', `noconstant'
}
*******************************************************************************************
* GMM with arbitrary weighting matrix
if ("`wmatrix'"~="") & ("`gmm'"=="") & ("`liml'`kclass'`cue'"=="") & "`b0'"=="" {
mat `B'=`wB'
scalar `rss'=`wbrss'
scalar `sigmasq'=`wb_s2'
* Weighting matrix wmatrix already checked and assigned to macro W
}
*******************************************************************************************
* IV coefficients
if ("`wmatrix'"=="") & ("`gmm'"=="") & ("`liml'`kclass'`cue'"=="") & "`b0'"=="" {
mat `B'=`ivB'
scalar `rss'=`ivrss'
scalar `sigmasq'=`iv_s2'
* IV weighting matrix. By convention, no small-sample adjustment (consistent with S)
mat `W' = `ZZinv'*(`Nprec'-`dofminus')/`iv_s2'
}
*******************************************************************************************
* LIML, k-class coefficients
if "`liml'`kclass'" ~= "" {
mat `B'=`lB'
scalar `rss'=`lrss'
scalar `sigmasq'=`l_s2'
* No weighting matrix.
}
*******************************************************************************************
* Efficient GMM coefficients
if "`gmm'"!="" & ("`liml'`kclass'`cue'"=="") & "`b0'"=="" {
mat `B'=`gmmB'
capture drop `ysum'
qui matrix vecaccum `ysum' = `iota' `gresid2' /*
*/ `wtexp' if `touse', `noconstant'
scalar `rss'= `ysum'[1,1]
* Adjust sigma-squared for dofminus
scalar `sigmasq'=`rss'/(`Nprec'-`dofminus')
mat `W'=`Sinv'
}
*******************************************************************************************
* Var-cov matrix
*******************************************************************************************
* Expressions below multipy by N because we are working with cross-products (XZ) not vcvs (Qxz)
* Efficient GMM: homoskedastic IV, 2-step FEGMM. LIML, k-class, CUE handled separately.
* No robust, cluster, kernel => must be efficient GMM
* GMM option => must be efficient GMM
* b0 => must be efficient GMM
* ewmatrix => must be efficient GMM
tempname rankV
if ("`robust'`cluster'`kernel'`liml'`kclass'`cue'`wmatrix'"=="") /*
*/ | ("`gmm'"~="") /*
*/ | ("`b0'"~="") /*
*/ | ("`ewmatrix'"~="") {
mat `tempmat'=`XZ'*`Sinv'*`XZ''
mat `tempmat'=(`tempmat'+`tempmat'')/2
mat `V' = syminv(`tempmat')*`Nprec'
mat `V'=(`V'+`V'')/2
scalar `rankV'=rowsof(`tempmat') - diag0cnt(`tempmat')
}
* Possibly inefficient GMM: robust of all sorts with no 2nd step. LIML, k-class, CUE handled separately.
else if ("`liml'`kclass'`cue'"=="") {
mat `V'=`XZWZXinv'*`XZW'*`S'* /*
*/ `XZW''*`XZWZXinv'*`Nprec'
mat `V'=(`V'+`V'')/2
mat `tempmat'=syminv(`V')
scalar `rankV'=rowsof(`tempmat') - diag0cnt(`tempmat')
}
* LIML and k-class non-robust
else if ("`liml'`kclass'" ~= "") & ("`robust'`cluster'" == "") {
if "`coviv'"== "" {
* LIML or k-class cov matrix
mat `V'=`sigmasq'*`XhXhinv'
scalar `rankV'=rowsof(`XhXh') - diag0cnt(`XhXh')
}
else {
* IV cov matrix
mat `V'=`sigmasq'*`XPZXinv'
scalar `rankV'=rowsof(`XPZXinv') - diag0cnt(`XPZXinv')
}
mat `V'=(`V'+`V'')/2
}
* LIML and k-class robust
else if ("`liml'`kclass'" ~= "") & ("`robust'`cluster'" ~= "") {
if "`coviv'"== "" {
* Use LIML or k-class cov matrix
mat `V'=`XhXhinv'*`XZ'*`ZZinv'*`S'*`Nprec'* /*
*/ `ZZinv'*`XZ''*`XhXhinv'
}
else {
* Use IV cov matrix
mat `V'=`XPZXinv'*`XZ'*`ZZinv'*`S'*`Nprec'* /*
*/ `ZZinv'*`XZ''*`XPZXinv'
}
mat `V'=(`V'+`V'')/2
mat `tempmat'=syminv(`V')
scalar `rankV'=rowsof(`tempmat') - diag0cnt(`tempmat')
}
* Model df handled here since it depends on rank of V
* CUE handled separately
if "`cue'"=="" {
if "`noconstant'"=="" {
local df_m = `rankV' - 1
}
else {
local df_m = `rankV'
}
}
* End of VCV block
********************************************************************************
* Sargan-Hansen-Anderson-Rubin statistics
*******************************************************************************************
* Robust requires using gmm residuals; otherwise use iv residuals. CUE handled separately.
* b0 => return value of CUE objective function. b0 is efficient GMM.
if ("`robust'`cluster'`kernel'" == "") & ("`cue'"=="") & ("`b0'`ewmatrix'"=="") {
mat `uZSinvZu'= (`s1Zu'/`Nprec')*`Sinv'*(`s1Zu''/`Nprec')
scalar `j' = `Nprec'*`uZSinvZu'[1,1]
}
if ("`robust'`cluster'`kernel'" ~= "") & ("`cue'"=="") & ("`b0'`ewmatrix'"=="") {
mat `uZSinvZu'= (`s2Zu'/`Nprec')*`Sinv'*(`s2Zu''/`Nprec')
scalar `j' = `Nprec'*`uZSinvZu'[1,1]
}
if "`b0'`ewmatrix'"~="" {
mat `uZSinvZu'= (`s1Zu'/`Nprec')*`Sinv'*(`s1Zu''/`Nprec')
scalar `j' = `Nprec'*`uZSinvZu'[1,1]
}
if "`liml'" != "" {
* Also save Anderson-Rubin overid stat if LIML
* Note dofminus is required because unlike Sargan and 2-step GMM J, doesn't derive from S
scalar `arubin'=(`Nprec'-`dofminus')*ln(`lambda')
}
***************************************************************************************
* Block for cue gmm
*******************************************************************************************
if "`cue'" != "" {
* Set up variables and options as globals
global IV_lhs "`lhs'"
global IV_inexog "`inexog1'"
global IV_endog "`endo1'"
global IV_exexog "`exexog1'"
global IV_wt "`wtexp'"
global IV_opt "`noconstant' `robust' `clopt' `bwopt' `kernopt' `dofmopt'"
* `gmm' not in IV_opt because cue+gmm not allowed
* Initial values use 2-step GMM if robust
if "`robust'`cluster'"~="" {
local init_opt "gmm"
}
tempname b_init temphold
capture _estimates hold `temphold', restore
if _rc==1000 {
di as err "ivreg2 internal error - no room to save temporary estimation results"
di as err "Please drop one or more estimation results using -estimates drop-"
exit 1000
}
qui `ivreg2_cmd' $IV_lhs $IV_inexog ($IV_endog=$IV_exexog) $IV_wt /*
*/ if `touse', $IV_opt `init_opt' noid
* Trap here if just-identified
if e(rankzz)>e(rankxx) {
if "`cueinit'"== "" {
mat `b_init'=e(b)
}
else {
mat `b_init'=`cueinit'
}
* Use ML for numerical optimization
ml model d0 `ivreg2_cmd'_cue ($IV_lhs = $IV_endog $IV_inexog, `noconstant') $IV_wt /*
*/ if `touse', maximize init(`b_init') `cueoptions' /*
*/ crittype(neg GMM obj function -J) /*
*/ collinear nooutput nopreserve missing noscvars
}
else {
di in ye "Equation exactly-identified: CUE and 2-step GMM coincide"
}
* Remove equation number from column names
mat `B'=e(b)
mat colnames `B' = _:
* Last call to get vcv, j, Sinv etc.
qui `ivreg2_cmd' $IV_lhs $IV_inexog ($IV_endog=$IV_exexog) $IV_wt /*
*/ if `touse', $IV_opt b0(`B') noid
* Save all results
mat `V'=e(V)
mat `S'=e(S)
mat `Sinv'=syminv(`S')
mat `W'=`Sinv'
local rankS = e(rankS)
scalar `j'=e(j)
local df_m = e(df_m)
scalar `rankV'=e(rankV)
if "`cluster'" != "" {
local N_clust=e(N_clust)
}
capture drop `yhat'
qui mat score double `yhat'=`B' if `touse'
qui gen double `gresid'=`lhs'-`yhat'
qui gen double `gresid2'=`gresid'^2
capture drop `ysum'
qui matrix vecaccum `ysum' = `iota' `gresid2' /*
*/ `wtexp' if `touse', `noconstant'
scalar `rss'= `ysum'[1,1]
* Adjust sigma-squared for dofminus
scalar `sigmasq'=`rss'/(`Nprec'-`dofminus')
macro drop IV_lhs IV_inexog IV_endog IV_exexog IV_wt IV_opt
capture _estimates unhold `temphold'
}
*******************************************************************************************
* RSS, counts, dofs, F-stat, small-sample corrections
*******************************************************************************************
scalar `rmse'=sqrt(`sigmasq')
if "`noconstant'"=="" {
scalar `mss'=`yyc' - `rss'
}
else {
scalar `mss'=`yy' - `rss'
}
* Counts modified to include constant if appropriate
if "`noconstant'"=="" {
local iv1_ct = `iv1_ct' + 1
local rhs1_ct = `rhs1_ct' + 1
}
* Correct count of rhs variables accounting for dropped collinear vars
* Count includes constant
local rhs_ct = rowsof(`XX') - diag0cnt(`XXinv')
if "`cluster'"=="" {
* Residual dof adjusted for dofminus
local df_r = `N' - `rhs_ct' - `dofminus'
}
else {
* To match Stata, subtract 1 (why 1 and not `rhs_ct' is a mystery)
local df_r = `N_clust' - 1
}
* Sargan-Hansen J dof and p-value
* df=0 doesn't guarantee j=0 since can be call to get value of CUE obj fn
local jdf = `iv_ct' - `rhs_ct'
if `jdf' == 0 & "`b0'"=="" {
scalar `j' = 0
}
else {
scalar `jp' = chiprob(`jdf',`j')
}
if "`liml'"~="" {
scalar `arubinp' = chiprob(`jdf',`arubin')
}
* Small sample corrections for var-cov matrix.
* If robust, the finite sample correction is N/(N-K), and with no small
* we change this to 1 (a la Davidson & MacKinnon 1993, p. 554, HC0).
* If cluster, the finite sample correction is (N-1)/(N-K)*M/(M-1), and with no small
* we change this to 1 (a la Wooldridge 2002, p. 193), where M=number of clusters.
* In the adj of the V matrix for non-small, we use Nprec instead of N because
* iweights rounds off N. Note that iweights are not allowed with robust
* but we use Nprec anyway to maintain consistency of code.
if "`small'" != "" {
if "`cluster'"=="" {
matrix `V'=`V'*(`Nprec'-`dofminus')/(`Nprec'-`rhs_ct'-`dofminus')
}
else {
matrix `V'=`V'*(`Nprec'-1)/(`Nprec'-`rhs_ct') /*
*/ * `N_clust'/(`N_clust'-1)
}
scalar `sigmasq'=`rss'/(`Nprec'-`rhs_ct'-`dofminus')
scalar `rmse'=sqrt(`sigmasq')
}
scalar `r2u'=1-`rss'/`yy'
scalar `r2c'=1-`rss'/`yyc'
if "`noconstant'"=="" {
scalar `r2'=`r2c'
scalar `r2_a'=1-(1-`r2')*(`Nprec'-1)/(`Nprec'-`rhs_ct'-`dofminus')
}
else {
scalar `r2'=`r2u'
scalar `r2_a'=1-(1-`r2')*`Nprec'/(`Nprec'-`rhs_ct'-`dofminus')
}
* Fstat
* To get it to match Stata's, must post separately with dofs and then do F stat by hand
* in case weights generate non-integer obs and dofs
* Create copies so they can be posted
tempname FB FV
mat `FB'=`B'
mat `FV'=`V'
capture ereturn post `FB' `FV'
* If the cov matrix wasn't positive definite, the post fails with error code 506
local rc = _rc
if `rc' != 506 {
local Frhs1 `rhs1'
capture test `Frhs1'
if "`small'" == "" {
if "`cluster'"=="" {
capture scalar `F' = r(chi2)/`df_m' * `df_r'/(`Nprec'-`dofminus')
}
else {
capture scalar `F' = r(chi2)/`df_m' * /*
* fwl_ct used here so that F-stat matches test stat from regression with no FWL and small
*/ (`N_clust'-1)/`N_clust' * (`Nprec'-`rhs_ct'-`fwl_ct')/(`Nprec'-1)
}
}
else {
capture scalar `F' = r(chi2)/`df_m'
}
capture scalar `Fp'=Ftail(`df_m',`df_r',`F')
capture scalar `Fdf2'=`df_r'
}
* If j==. or vcv wasn't full rank, then vcv problems and F is meaningless
if `j' == . | `rc'==506 {
scalar `F' = .
scalar `Fp' = .
}
* End of counts, dofs, F-stat, small sample corrections
*******************************************************************************************
* orthog option: C statistic (difference of Sargan statistics)
*******************************************************************************************
* Requires j dof from above
if "`orthog'"!="" {
tempname cj cstat cstatp
* Initialize cstat
scalar `cstat' = 0
* Each variable listed must be in instrument list.
* To avoid overwriting, use cendo, cinexog1, cexexog, cendo_ct, cex_ct
local cendo1 "`endo1'"
local cinexog1 "`inexog1'"
local cexexog1 "`exexog1'"
local cinsts1 "`insts1'"
local crhs1 "`rhs1'"
local clist1 "`orthog'"
local clist_ct : word count `clist1'
* Check to see if c-stat vars are in original list of all ivs
* cinexog1 and cexexog1 are after c-stat exog list vars have been removed
* cendo1 is endo1 after included exog being tested has been added
foreach x of local clist1 {
local llex_ct : word count `cexexog1'
Subtract cexexog1 : "`cexexog1'" "`x'"
local cex1_ct : word count `cexexog1'
local ok = `llex_ct' - `cex1_ct'
if (`ok'==0) {
* Not in excluded, check included and add to endog list if it appears
local llin_ct : word count `cinexog1'
Subtract cinexog1 : "`cinexog1'" "`x'"
local cin1_ct : word count `cinexog1'
local ok = `llin_ct' - `cin1_ct'
if (`ok'==0) {
* Not in either list
di in r "Error: `x' listed in orthog() but does not appear as exogenous."
error 198
}
else {
local cendo1 "`cendo1' `x'"
}
}
}
* If robust, HAC/AC or GMM (but not LIML or IV), create optimal weighting matrix to pass to ivreg2
* by extracting the submatrix from the full S and then inverting.
* This guarantees the C stat will be non-negative. See Hayashi (2000), p. 220.
* Calculate C statistic with recursive call to ivreg2
* Collinearities may cause problems, hence -capture-.
* smatrix works generally, including homoskedastic case with Sargan stat
capture {
capture _estimates hold `ivest', restore
if _rc==1000 {
di as err "ivreg2 internal error - no room to save temporary estimation results"
di as err "Please drop one or more estimation results using -estimates drop-"
exit 1000
}
if "`kernel'" != "" {
local bwopt "bw(`bw')"
local kernopt "kernel(`kernel')"
}
* clopt is omitted because it requires calculation of numbers of clusters, which is done
* only when S matrix is calculated
capture `ivreg2_cmd' `lhs' `cinexog1' /*
*/ (`cendo1'=`cexexog1') /*
*/ if `touse' `wtexp', `noconstant' /*
*/ `options' `small' `robust' /*
*/ `gmm' `bwopt' `kernopt' `dofmopt' `sw' `swpsd' /*
*/ smatrix("`S'") noid
local rc = _rc
if `rc' == 481 {
scalar `cstat' = 0
local cstatdf = 0
}
else {
scalar `cj'=e(j)
local cjdf=e(jdf)
}
scalar `cstat' = `j' - `cj'
local cstatdf = `jdf' - `cjdf'
_estimates unhold `ivest'
scalar `cstatp'= chiprob(`cstatdf',`cstat')
* Collinearities may cause C-stat dof to differ from the number of variables in orthog()
* If so, set cstat=0
if `cstatdf' != `clist_ct' {
scalar `cstat' = 0
}
}
}
* End of orthog block
*******************************************************************************************
* Endog option
*******************************************************************************************
* Uses recursive call with orthog
if "`endogtest'"!="" {
tempname estat estatp
* Initialize estat
scalar `estat' = 0
* Each variable to test must be in endo list.
* To avoid overwriting, use eendo, einexog1, etc.
local eendo1 "`endo1'"
local einexog1 "`inexog1'"
local einsts1 "`insts1'"
local elist1 "`endogtest'"
local elist_ct : word count `elist1'
* Check to see if endog test vars are in original endo1 list of endogeneous variables
* eendo1 and einexog1 are after endog test vars have been removed from endo and added to inexog
foreach x of local elist1 {
local llendo_ct : word count `eendo1'
local eendo1 : list eendo1 - x
local eendo1_ct : word count `eendo1'
local ok = `llendo_ct' - `eendo1_ct'
if (`ok'==0) {
* Not in endogenous list
di in r "Error: `x' listed in endog() but does not appear as endogenous."
error 198
}
else {
local einexog1 "`einexog1' `x'"
}
}
* Recursive call to ivreg2 using orthog option to obtain endogeneity test statistic
* Collinearities may cause problems, hence -capture-.
capture {
capture _estimates hold `ivest', restore
if _rc==1000 {
di as err "ivreg2 internal error - no room to save temporary estimation results"
di as err "Please drop one or more estimation results using -estimates drop-"
exit 1000
}
capture `ivreg2_cmd' `lhs' `einexog1' /*
*/ (`eendo1'=`exexog1') if `touse' /*
*/ `wtexp', `noconstant' `robust' `clopt' /*
*/ `gmm' `liml' `bwopt' `kernopt' /*
*/ `small' `dofmopt' `sw' `swpsd' `options' /*
*/ orthog(`elist1') noid
local rc = _rc
if `rc' == 481 {
scalar `estat' = 0
local estatdf = 0
}
else {
scalar `estat'=e(cstat)
local estatdf=e(cstatdf)
scalar `estatp'=e(cstatp)
}
_estimates unhold `ivest'
* Collinearities may cause endog stat dof to differ from the number of variables in endog()
* If so, set estat=0
if `estatdf' != `elist_ct' {
scalar `estat' = 0
}
}
* End of endogeneity test block
}
*******************************************************************************************
* Rank identification and redundancy block
*******************************************************************************************
if `endo1_ct' > 0 & "`noid'"=="" {
tempname ccmat ccrealev ccimagev cc idstat iddf idp
tempname cdchi2 cdchi2p ccf cdf cdeval cd
mat `ccmat' = `XXinv'*`XPZX'
* Need only upper LHS block, which corresponds to included endogenous
mat `ccmat' = `ccmat'[1..`endo1_ct',1..`endo1_ct']
mat eigenvalues `ccrealev' `ccimagev' = `ccmat'
* Real eigenvalues are the squared canonical correlations
* The first reported cc is NOT necessarily the smallest (with mat symeigen the smallest is last).
* Sort so smallest is first.
vecsort `ccrealev'
scalar `cc'=`ccrealev'[1,1]
* dof adjustment needed because it doesn't use the adjusted S
scalar `idstat' = -(`Nprec'-`dofminus')*ln(1-`cc')
local iddf = `iv_ct' - (`rhs_ct'-1)
scalar `idp' = chiprob(`iddf',`idstat')
* Cragg-Donald, Anderson etc.
scalar `cd'=`cc'/(1-`cc')
* dofminus used because it doesn't use adjusted S
local ddf = `Nprec'-`iv_ct'-`dofminus'
local ndf = `exex1_ct'
scalar `cdchi2'=`cd'*(`Nprec'-`dofminus')
scalar `cdchi2p' = chiprob(`iddf',`cdchi2')
scalar `cdf' =`cd'*`ddf'/`ndf'
scalar `ccf' =`cc'*`ddf'/`ndf'
* Save evs in CD style
local evcols = colsof(`ccrealev')
mat `cdeval' = J(1,`evcols',.)
forval i=1/`evcols' {
mat `cdeval'[1,`i'] = `ccrealev'[1,`i'] / (1 - `ccrealev'[1,`i'])
}
}
* LR redundancy test
if `endo1_ct' > 0 & "`redundant'" ~= "" & "`noid'"=="" {
* Obtain Anderson zero rank (totally unidentified) statistic for full set of instruments
tempname unidstat
scalar `unidstat'=0
forvalues thiscol=1(1)`endo1_ct' {
* dof adjustment needed because it doesn't use the adjusted S
scalar `unidstat'=`unidstat'-(`Nprec'-`dofminus')*ln(1-`ccrealev'[1,`thiscol'])
}
* Diff between this and the stat using the irrelevant excl IVs is chi2 with dof=#endog*#tested
local redlist1 "`redundant'"
* XZcols are the Z columns, so can use for ZZ too
local rXZcols : colnames `XZ'
foreach x of local redlist1 {
local riv_ct_a : word count `rXZcols'
Subtract rXZcols : "`rXZcols'" "`x'"
local riv_ct_b : word count `rXZcols'
if `riv_ct_a' == `riv_ct_b' {
* Not in list
di in r "Error: `x' listed in redundant() but does not appear as excluded instrument."
error 198
}
}
tempname rXZ rZZ rZZtemp rZZinv rXPZX rccmat rccrealev rccimagev runidmat runidstat
foreach cn of local rXZcols {
mat `rXZ' = nullmat(`rXZ') , `XZ'[1...,"`cn'"]
mat `rZZtemp' = nullmat(`rZZtemp') , `ZZ'[1...,"`cn'"]
}
foreach cn of local rXZcols {
mat `rZZ' = nullmat(`rZZ') \ `rZZtemp'["`cn'",1...]
}
mat `rZZ'=(`rZZ'+`rZZ'')/2
mat `rZZinv' = syminv(`rZZ')
mat `rXPZX' = `rXZ'*`rZZinv'*`rXZ''
mat `rccmat' = `XXinv'*`rXPZX'
mat `rccmat' = `rccmat'[1..`endo1_ct',1..`endo1_ct']
mat eigenvalues `rccrealev' `rccimagev' = `rccmat'
scalar `runidstat'=0
forvalues thiscol=1(1)`endo1_ct' {
* dof adjustment needed because it doesn't use the adjusted S
scalar `runidstat'=`runidstat'-(`Nprec'-`dofminus')*ln(1-`rccrealev'[1,`thiscol'])
}
tempname redstat redp
local riv_ct = rowsof(`rZZ') - diag0cnt(`rZZinv')
if `riv_ct' < `rhs_ct' {
* Not in list
di in r "Error: specification with redundant() option is unidentified (fails rank condition)"
error 198
}
local redlist_ct=`iv_ct'-`riv_ct'
scalar `redstat' = `unidstat' - `runidstat'
local reddf = `endo1_ct'*`redlist_ct'
scalar `redp' = chiprob(`reddf',`redstat')
}
* End of identification stats block
*******************************************************************************************
* Error-checking block
*******************************************************************************************
* Check if adequate number of observations
if `N' <= `iv_ct' {
di in r "Error: number of observations must be greater than number of instruments"
di in r " including constant."
error 2001
}
* Check if robust VCV matrix is of full rank
if "`gmm'`robust'`cluster'`kernel'" != "" {
* Robust covariance matrix not of full rank means either a singleton dummy or too few
* clusters (in which case the indiv SEs are OK but no F stat or 2-step GMM is possible),
* or there are too many AC/HAC-lags, or the HAC covariance estimator
* isn't positive definite (possible with truncated and Tukey-Hanning kernels)
if `rankS' < `iv_ct' {
* If two-step GMM then exit with error ...
if "`gmm'" != "" {
di in r "Error: estimated covariance matrix of moment conditions not of full rank;"
di in r " cannot calculate optimal weighting matrix for GMM estimation."
di in r "Possible causes:"
if "`cluster'" != "" {
di in r " number of clusters insufficient to calculate optimal weighting matrix"
}
if "`kernel'" != "" {
di in r " covariance matrix of moment conditions not positive definite"
di in r " covariance matrix uses too many lags"
}
di in r " singleton dummy variable (dummy with one 1 and N-1 0s or vice versa)"
di in r "-fwl- option may address problem. See help " _c
di in smcl "{help ivreg2}".
error 498
}
* Estimation isn't two-step GMM so continue but J, F, and C stat (if present) all meaningless
* Must set Sargan-Hansen j = missing so that problem can be reported in output
else {
scalar `j' = .
if "`orthog'"!="" {
scalar `cstat' = .
}
if "`endogtest'"!="" {
scalar `estat' = .
}
}
}
}
* End of error-checking block
********************************************************************************************
* Reduced form and first stage regression options
*******************************************************************************************
* Relies on proper count of (non-collinear) IVs generated earlier.
* Note that nocons option + constant in instrument list means first-stage
* regressions are reported with nocons option. First-stage F-stat therefore
* correctly includes the constant as an explanatory variable.
if "`rf'`saverf'`first'`ffirst'`savefirst'" != "" & (`endo1_ct' > 0) & "`noid'"=="" {
* Reduced form needed for AR first-stage test stat. Also estimated if requested.
tempname archi2 archi2p arf arfp ardf ardf_r sstat sstatp sstatdf
doRF "`lhs'" "`inexog1'" "`exexog1'" /*
*/ `touse' `"`wtexp'"' `"`noconstant'"' `"`robust'"' /*
*/ `"`clopt'"' `"`bwopt'"' `"`kernopt'"' /*
*/ `"`saverfprefix'"' /*
*/ "`dofminus'" `"`sw'"' `"`swpsd'"' "`ivreg2_cmd'"
scalar `archi2'=r(archi2)
scalar `archi2p'=r(archi2p)
scalar `arf'=r(arf)
scalar `arfp'=r(arfp)
scalar `ardf'=r(ardf)
scalar `ardf_r'=r(ardf_r)
local rfeq "`r(rfeq)'"
* Drop saved rf results if needed only for first-stage estimations
if "`rf'`saverf'" == "" {
capture estimates drop `rfeq'
}
* Stock-Wright S statistic. Equiv to J LM test of exexog.
* First block handles all cases except no exog regressors; second block uses GMM obj function,
* which works without fwl because b0 is only endog regressors.
if `inexog1_ct' + `cons_ct' > 0 {
qui `ivreg2_cmd' `lhs' `inexog' (=`exexog') `wtexp' if `touse', /*
*/ `noconstant' dofminus(`dofminus') /*
*/ `robust' `clopt' `bwopt' `kernopt' `sw' `swpsd'
}
else {
tempname b1
mat `b1'=J(1,`endo1_ct',0)
matrix colnames `b1' = `endo1'
qui `ivreg2_cmd' `lhs' (`endo1'=`exexog') `wtexp' if `touse', /*
*/ b0(`b1') noconstant dofminus(`dofminus') /*
*/ `robust' `clopt' `bwopt' `kernopt' `sw' `swpsd'
}
scalar `sstat'=e(j)
scalar `sstatdf'=`ardf'
scalar `sstatp'=chiprob(`sstatdf',`sstat')
}
if "`first'`ffirst'`savefirst'" != "" & (`endo1_ct' > 0) {
* Godfrey method of Shea partial R2 uses IV and OLS estimates without robust vcvs:
* Partial R2 = OLS V[d,d] / IV V[d,d] * IV s2 / OLS s2
* where d,d is the diagonal element corresponding to the endog regressor
* ... but this simplifies to matrices that have already been calculated:
* = XXinv[d,d] / XPZXinv[d,d]
tempname godfrey sols siv
tempname firstmat sheapr2 pr2 pr2F pr2p
mat `godfrey' = J(1,`endo1_ct',0)
mat colnames `godfrey' = `endo1'
mat rownames `godfrey' = "sheapr2"
local i 1
foreach var of local endo1 {
mat `sols'=`XXinv'["`var'","`var'"]
mat `siv'=`XPZXinv'["`var'","`var'"]
mat `godfrey'[1,`i'] = `sols'[1,1]/`siv'[1,1]
local i = `i'+1
}
if `iv1_ct' > `iv_ct' {
di
di in gr "Warning: collinearities detected among instruments"
di in gr "1st stage tests of excluded exogenous variables may be incorrect"
}
doFirst "`endo1'" "`inexog1'" "`exexog1'" /*
*/ `touse' `"`wtexp'"' `"`noconstant'"' `"`robust'"' /*
*/ `"`clopt'"' `"`bwopt'"' `"`kernopt'"' /*
*/ `"`savefprefix'"' /*
*/ `"`dofmopt'"' `"`sw'"' `"`swpsd'"' "`ivreg2_cmd'"
local firsteqs "`r(firsteqs)'"
capture mat `firstmat'=`godfrey' \ r(firstmat)
if _rc != 0 {
di in ye "Warning: missing values encountered; first stage regression results not saved"
}
}
* End of first-stage regression code
**********************************************************************************************
* Post and display results.
*******************************************************************************************
* restore data if preserved for fwl option
if "`fwl'" != "" {
restore
}
* NB: Would like to use -Nprec- in obs() in case weights generate non-integer obs
* but Stata complains. Using -Nprec- with dof() makes no difference - seems to round it
if "`small'"!="" {
local NminusK = `N'-`rhs_ct'
capture ereturn post `B' `V', dep(`depname') obs(`N') esample(`touse') /*
*/ dof(`NminusK')
}
else {
capture ereturn post `B' `V', dep(`depname') obs(`N') esample(`touse')
}
local rc = _rc
if `rc' == 504 {
di in red "Error: estimated variance-covariance matrix has missing values"
exit 504
}
if `rc' == 506 {
di in red "Error: estimated variance-covariance matrix not positive-definite"
exit 506
}
if `rc' > 0 {
di in red "Error: estimation failed - could not post estimation results"
exit `rc'
}
* changed next from `endo1' to `endo' 2Aug06 MES
ereturn local instd `endo'
local insts : colnames `S'
local insts : subinstr local insts "_cons" ""
ereturn local insts `insts'
ereturn local inexog `inexog'
ereturn local exexog `exexog'
ereturn local fwl `fwl'
ereturn scalar inexog_ct=`inexog1_ct'
ereturn scalar exexog_ct=`exex1_ct'
ereturn scalar endog_ct =`endo1_ct'
if "`collin'`ecollin'`dups'`fwlcons'" != "" {
ereturn local collin `collin'
ereturn local ecollin `ecollin'
ereturn local dups `dups'
ereturn local instd1 `endo1'
ereturn local inexog1 `inexog1'
ereturn local exexog1 `exexog1'
ereturn local fwl1 `fwl1'
}
if "`smatrix'" == "" {
ereturn matrix S `S'
}
else {
* Create a copy so posting doesn't zap the original
tempname Scopy
mat `Scopy'=`smatrix'
ereturn matrix S `Scopy'
}
* No weighting matrix defined for LIML and kclass
if "`wmatrix'"=="" & "`liml'`kclass'"=="" {
ereturn matrix W `W'
}
else if "`liml'`kclass'"=="" {
* Create a copy so posting doesn't zap the original
tempname Wcopy
mat `Wcopy'=`wmatrix'
ereturn matrix W `Wcopy'
}
if "`kernel'"!="" {
ereturn local kernel "`kernel'"
ereturn scalar bw=`bw'
ereturn local tvar "`tvar'"
if "`ivar'" ~= "" {
ereturn local ivar "`ivar'"
}
}
if "`small'"!="" {
ereturn scalar df_r=`df_r'
ereturn local small "small"
}
if "`cluster'"!="" {
ereturn scalar N_clust=`N_clust'
ereturn local clustvar `cluster'
}
if "`robust'`cluster'" != "" {
ereturn local vcetype "Robust"
}
ereturn scalar df_m=`df_m'
ereturn scalar r2=`r2'
ereturn scalar rmse=`rmse'
ereturn scalar rss=`rss'
ereturn scalar mss=`mss'
ereturn scalar r2_a=`r2_a'
ereturn scalar F=`F'
ereturn scalar Fp=`Fp'
ereturn scalar Fdf2=`Fdf2'
ereturn scalar yy=`yy'
ereturn scalar yyc=`yyc'
ereturn scalar r2u=`r2u'
ereturn scalar r2c=`r2c'
ereturn scalar rankzz=`iv_ct'
ereturn scalar rankxx=`rhs_ct'
if "`gmm'`robust'`cluster'`kernel'" != "" {
ereturn scalar rankS=`rankS'
}
ereturn scalar rankV=`rankV'
ereturn scalar ll = -0.5 * (`Nprec'*ln(2*_pi) + `Nprec'*ln(`rss'/`Nprec') + `Nprec')
* Always save J. Also save as Sargan if homoskedastic; save A-R if LIML.
ereturn scalar j=`j'
ereturn scalar jdf=`jdf'
if `j' != 0 & `j' != . {
ereturn scalar jp=`jp'
}
if ("`robust'`cluster'"=="") {
ereturn scalar sargan=`j'
ereturn scalar sargandf=`jdf'
if `j' != 0 & `j' != . {
ereturn scalar sarganp=`jp'
}
}
if "`liml'"!="" {
ereturn scalar arubin=`arubin'
ereturn scalar arubindf=`jdf'
if `j' != 0 & `j' != . {
ereturn scalar arubinp=`arubinp'
}
}
if "`orthog'"!="" {
ereturn scalar cstat=`cstat'
if `cstat'!=0 & `cstat' != . {
ereturn scalar cstatp=`cstatp'
ereturn scalar cstatdf=`cstatdf'
ereturn local clist `clist1'
}
}
if "`endogtest'"!="" {
ereturn scalar estat=`estat'
if `estat'!=0 & `estat' != . {
ereturn scalar estatp=`estatp'
ereturn scalar estatdf=`estatdf'
ereturn local elist `elist1'
}
}
if `endo1_ct' > 0 & "`noid'"=="" {
ereturn scalar idstat=`idstat'
ereturn scalar iddf=`iddf'
ereturn scalar idp=`idp'
ereturn scalar cd=`cd'
ereturn scalar cdf=`cdf'
ereturn matrix ccev=`ccrealev'
capture ereturn matrix cdev `cdeval'
}
if "`redundant'"!="" & "`noid'"=="" {
ereturn scalar redstat=`redstat'
ereturn scalar redp=`redp'
ereturn scalar reddf=`reddf'
ereturn local redlist `redlist1'
}
if "`first'`ffirst'`savefirst'" != "" & `endo1_ct'>0 & "`noid'"=="" {
* Capture here because firstmat empty if mvs encountered in 1st stage regressions
capture ereturn matrix first `firstmat'
ereturn scalar cdchi2=`cdchi2'
ereturn scalar cdchi2p=`cdchi2p'
ereturn scalar arf=`arf'
ereturn scalar arfp=`arfp'
ereturn scalar archi2=`archi2'
ereturn scalar archi2p=`archi2p'
ereturn scalar ardf=`ardf'
ereturn scalar ardf_r=`ardf_r'
ereturn scalar sstat=`sstat'
ereturn scalar sstatp=`sstatp'
ereturn scalar sstatdf=`sstatdf'
ereturn local firsteqs `firsteqs'
}
if "`rf'`saverf'" != "" & `endo1_ct'>0 {
ereturn local rfeq `rfeq'
}
ereturn local depvar `lhs'
if "`liml'"!="" {
ereturn local model "liml"
ereturn scalar kclass=`kclass2'
ereturn scalar lambda=`lambda'
if `fuller' > 0 & `fuller' < . {
ereturn scalar fuller=`fuller'
}
}
else if "`kclass'" != "" {
ereturn local model "kclass"
ereturn scalar kclass=`kclass2'
}
else if "`gmm'`cue'`wmatrix'"=="" {
if "`endo1'" == "" {
ereturn local model "ols"
}
else {
ereturn local model "iv"
}
}
else if "`cue'"~="" {
ereturn local model "cue"
}
else if "`wmatrix'"~="" {
ereturn local model "gmm"
}
else if "`gmm'"~="" {
ereturn local model "gmm2s"
}
else {
* Should never enter here
ereturn local model "unknown"
}
if "`weight'" != "" {
ereturn local wexp "=`exp'"
ereturn local wtype `weight'
}
ereturn local cmd `ivreg2_cmd'
ereturn local version `lversion'
if "`noconstant'"!="" {
ereturn scalar cons=0
}
else {
ereturn scalar cons=1
}
if `fwl_ct'>0 {
ereturn scalar fwlcons=`fwlcons'
}
ereturn local predict "`ivreg2_cmd'_p"
if "`e(model)'"=="gmm2s" {
local title2 "2-Step GMM estimation"
}
if "`e(model)'"=="gmm" {
local title2 "GMM estimation with user-supplied weighting matrix"
}
if "`e(model)'"=="cue" {
local title2 "CUE estimation"
}
if "`e(model)'"=="ols" {
local title2 "OLS estimation"
}
if "`e(model)'"=="iv" {
local title2 "IV (2SLS) estimation"
}
if "`e(model)'"=="liml" {
local title2 "LIML estimation"
}
if "`e(model)'"=="kclass" {
local title2 "k-class estimation"
}
if "`e(vcetype)'" == "Robust" {
local hacsubtitle1 "heteroskedasticity"
}
if "`e(kernel)'"!="" {
local hacsubtitle3 "autocorrelation"
}
if "`e(clustvar)'"!="" {
local hacsubtitle3 "clustering on `e(clustvar)'"
}
if "`hacsubtitle1'"~="" & "`hacsubtitle3'" ~= "" {
local hacsubtitle2 " and "
}
if "`title'"=="" {
ereturn local title "`title1'`title2'"
}
else {
ereturn local title "`title'"
}
if "`subtitle'"~="" {
ereturn local subtitle "`subtitle'"
}
local hacsubtitle "`hacsubtitle1'`hacsubtitle2'`hacsubtitle3'"
if "`hacsubtitle'"~="" {
ereturn local hacsubtitle "Statistics robust to `hacsubtitle'"
}
if "`sw'"~="" & "`swpsd'"=="" {
ereturn local hacsubtitle "Stock-Watson heteroskedastic-robust statistics (BETA VERSION)"
}
if "`swpsd'"~="" {
ereturn local hacsubtitle "Stock-Watson psd heteroskedastic-robust statistics (BETA VERSION)"
}
}
*******************************************************************************************
* Display results unless ivreg2 called just to generate stats or nooutput option
if "`nooutput'" == "" {
if "`savefirst'`saverf'" != "" {
DispStored `"`saverf'"' `"`savefirst'"' `"`ivreg2_cmd'"'
}
if "`rf'" != "" {
DispRF
}
if "`first'" != "" {
DispFirst `"`ivreg2_cmd'"'
}
if "`first'`ffirst'" != "" {
DispFFirst `"`ivreg2_cmd'"'
}
if "`eform'"!="" {
local efopt "eform(`eform')"
}
DispMain `"`noheader'"' `"`plus'"' `"`efopt'"' `"`level'"' `"`nofooter'"' `"`ivreg2_cmd'"'
}
* Drop first stage estimations unless explicitly saved or if replay
if "`savefirst'" == "" {
local firsteqs "`e(firsteqs)'"
foreach eqname of local firsteqs {
capture estimates drop `eqname'
}
ereturn local firsteqs
}
* Drop reduced form estimation unless explicitly saved or if replay
if "`saverf'" == "" {
local eqname "`e(rfeq)'"
capture estimates drop `eqname'
ereturn local rfeq
}
end
*******************************************************************************************
* SUBROUTINES
*******************************************************************************************
program define DispMain, eclass
args noheader plus efopt level nofooter helpfile
version 8.2
* Prepare for problem resulting from rank(S) being insufficient
* Results from insuff number of clusters, too many lags in HAC,
* to calculate robust S matrix, HAC matrix not PD, singleton dummy,
* and indicated by missing value for j stat
* Macro `rprob' is either 1 (problem) or 0 (no problem)
capture local rprob ("`e(j)'"==".")
if "`noheader'"=="" {
if "`e(title)'" ~= "" {
di in gr _n "`e(title)'"
local tlen=length("`e(title)'")
di in gr "{hline `tlen'}"
}
if "`e(subtitle)'" ~= "" {
di in gr "`e(subtitle)'"
}
if "`e(model)'"=="liml" | "`e(model)'"=="kclass" {
di in gr "k =" %7.5f `e(kclass)'
}
if "`e(model)'"=="liml" {
di in gr "lambda =" %7.5f `e(lambda)'
}
if e(fuller) > 0 & e(fuller) < . {
di in gr "Fuller parameter=" %-5.0f `e(fuller)'
}
if "`e(hacsubtitle)'" ~= "" {
di in gr _n "`e(hacsubtitle)'"
}
if "`e(kernel)'"!="" {
di in gr " kernel=`e(kernel)'; bandwidth=`e(bw)'"
di in gr " time variable (t): " in ye e(tvar)
if "`e(ivar)'" != "" {
di in gr " group variable (i): " in ye e(ivar)
}
}
di
if "`e(clustvar)'"!="" {
di in gr "Number of clusters (" "`e(clustvar)'" ") = " in ye %-4.0f e(N_clust) _continue
}
else {
di in gr " " _continue
}
di in gr _col(55) "Number of obs = " in ye %8.0f e(N)
if "`e(clustvar)'"=="" {
local Fdf2=e(N)-e(rankxx)
}
else {
local Fdf2=e(N_clust)-1
}
di in gr _c _col(55) "F(" %3.0f e(df_m) "," %6.0f e(Fdf2) ") = "
if e(F) < 99999 {
di in ye %8.2f e(F)
}
else {
di in ye %8.2e e(F)
}
di in gr _col(55) "Prob > F = " in ye %8.4f e(Fp)
di in gr "Total (centered) SS = " in ye %12.0g e(yyc) _continue
di in gr _col(55) "Centered R2 = " in ye %8.4f e(r2c)
di in gr "Total (uncentered) SS = " in ye %12.0g e(yy) _continue
di in gr _col(55) "Uncentered R2 = " in ye %8.4f e(r2u)
di in gr "Residual SS = " in ye %12.0g e(rss) _continue
di in gr _col(55) "Root MSE = " in ye %8.4g e(rmse)
di
}
* Display coefficients etc.
* Unfortunate but necessary hack here: to suppress message about cluster adjustment of
* standard error, clear e(clustvar) and then reset it after display
local cluster `e(clustvar)'
ereturn local clustvar
ereturn display, `plus' `efopt' level(`level')
ereturn local clustvar `cluster'
* Display 1st footer with identification stats
* Footer not displayed if -nofooter- option or if pure OLS, i.e., model="ols" and Sargan-Hansen=0
if ~("`nofooter'"~="" | (e(model)=="ols" & (e(sargan)==0 | e(j)==0))) {
* Report Anderson rank ID test
if "`e(instd)'"~="" & "`e(idstat)'"~="" {
di in smcl _c "{help `helpfile'##cancortest:Anderson canon. corr. LR statistic}"
di in gr _c " (underidentification test):"
di in ye _col(71) %8.3f e(idstat)
di in gr _col(52) "Chi-sq(" in ye e(iddf) /*
*/ in gr ") P-val = " in ye _col(73) %6.4f e(idp)
* LR IV redundancy statistic
if "`e(redlist)'"!="" {
di in gr "-redundant- option:"
di in smcl _c "{help `helpfile'##redtest:LR IV redundancy test}"
di in gr _c " (redundancy of specified instruments):"
di in ye _col(71) %8.3f e(redstat)
di in gr _col(52) "Chi-sq(" in ye e(reddf) /*
*/ in gr ") P-val = " in ye _col(73) %6.4f e(redp)
di in gr "Instruments tested: " _c
Disp `e(redlist)', _col(23)
}
if "`e(vcetype)'"=="Robust" | "`e(kernel)'"~="" {
di in gr "Test statistic(s) not robust"
}
di in smcl in gr "{hline 78}"
}
* Report Cragg-Donald statistic
if "`e(instd)'"~="" & "`e(idstat)'"~="" {
di in smcl _c "{help `helpfile'##cdtest:Cragg-Donald F statistic}"
di in gr _c " (weak identification test):"
di in ye _col(71) %8.3f e(cdf)
di in gr _c "Stock-Yogo weak ID test critical values:"
local cdmissing=1
if "`e(model)'"=="iv" | "`e(model)'"=="gmm2s" | "`e(model)'"=="gmm" {
cdsy, type(ivbias5) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(43) "5% maximal IV relative bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(ivbias10) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "10% maximal IV relative bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(ivbias20) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "20% maximal IV relative bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(ivbias30) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "30% maximal IV relative bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(ivsize10) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "10% maximal IV size" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(ivsize15) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "15% maximal IV size" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(ivsize20) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "20% maximal IV size" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(ivsize25) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "25% maximal IV size" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
}
else if ("`e(model)'"=="liml" & e(fuller)==.) | "`e(model)'"=="cue" {
cdsy, type(limlsize10) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "10% maximal LIML size" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(limlsize15) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "15% maximal LIML size" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(limlsize20) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "20% maximal LIML size" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(limlsize25) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "25% maximal LIML size" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
}
else if ("`e(model)'"=="liml" & e(fuller)<.) {
cdsy, type(fullrel5) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(43) "5% maximal Fuller rel. bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(fullrel10) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "10% maximal Fuller rel. bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(fullrel20) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "20% maximal Fuller rel. bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(fullrel30) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "30% maximal Fuller rel. bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(fullmax5) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(43) "5% Fuller maximum bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(fullmax10) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "10% Fuller maximum bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(fullmax20) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "20% Fuller maximum bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
cdsy, type(fullmax30) k2(`e(exexog_ct)') nendog(`e(endog_ct)')
if "`r(cv)'"~="." {
di in gr _col(42) "30% Fuller maximum bias" in ye _col(73) %6.2f r(cv)
local cdmissing=0
}
di in gr "NB: Critical values based on Fuller parameter=1"
}
if `cdmissing' {
di in gr _col(64) "<not available>"
}
else {
if "`e(vcetype)'"=="Robust" | "`e(kernel)'"~="" {
di in gr "Test statistic(s) not robust"
}
di in gr "Source: Stock-Yogo (2005). Reproduced by permission."
}
di in smcl in gr "{hline 78}"
}
* Report either (a) Sargan-Hansen-C stats, or (b) robust covariance matrix problem
if `rprob' == 0 {
* Display overid statistic
if "`e(vcetype)'" == "Robust" {
if "`e(instd)'" != "" {
di in smcl _c "{help `helpfile'##overidtests:Hansen J statistic}"
di in gr _c " (overidentification test of all instruments):"
}
else {
di in smcl _c "{help `helpfile'##overidtests:Hansen J statistic}"
di in gr _c " (Lagrange multiplier test of excluded instruments):"
}
}
else {
if "`e(instd)'" != "" {
di in smcl _c "{help `helpfile'##overidtests:Sargan statistic}"
di in gr _c " (overidentification test of all instruments):"
}
else {
di in smcl _c "{help `helpfile'##overidtests:Sargan statistic}"
di in gr _c " (Lagrange multiplier test of excluded instruments):"
}
}
di in ye _col(71) %8.3f e(j)
if e(rankxx) < e(rankzz) {
di in gr _col(52) "Chi-sq(" in ye e(jdf) /*
*/ in gr ") P-val = " in ye _col(73) %6.4f e(jp)
}
else {
di in gr _col(50) "(equation exactly identified)"
}
* Display orthog option: C statistic (difference of Sargan statistics)
if e(cstat) != . {
* If C-stat = 0 then warn, otherwise output
if e(cstat) > 0 {
di in gr "-orthog- option:"
if "`e(vcetype)'" == "Robust" {
di in gr _c "Hansen J statistic (eqn. excluding suspect orthog. conditions): "
}
else {
di in gr _c "Sargan statistic (eqn. excluding suspect orthogonality conditions):"
}
di in ye _col(71) %8.3f e(j)-e(cstat)
di in gr _col(52) "Chi-sq(" in ye e(jdf)-e(cstatdf) in gr ") P-val = " /*
*/ in ye _col(73) %6.4f chiprob(e(jdf)-e(cstatdf),e(j)-e(cstat))
di in smcl _c "{help `helpfile'##ctest:C statistic}"
di in gr _c " (exogeneity/orthogonality of suspect instruments): "
di in ye _col(71) %8.3f e(cstat)
di in gr _col(52) "Chi-sq(" in ye e(cstatdf) in gr ") P-val = " /*
*/ in ye _col(73) %6.4f e(cstatp)
di in gr "Instruments tested: " _c
Disp `e(clist)', _col(23)
}
if e(cstat) == 0 {
di in gr _n "Collinearity/identification problems in eqn. excl. suspect orthog. conditions:"
di in gr " C statistic not calculated for -orthog- option"
}
}
}
else {
* Problem exists with robust VCV - notify and list possible causes
di in r "Error: estimated covariance matrix of moment conditions not of full rank;"
di in r " overidentification statistic not reported, and standard errors and"
di in r " model tests should be interpreted with caution."
di in r "Possible causes:"
if e(N_clust) < e(rankzz) {
di in r " number of clusters insufficient to calculate robust covariance matrix"
}
if "`e(kernel)'" != "" {
di in r " covariance matrix of moment conditions not positive definite"
di in r " covariance matrix uses too many lags"
}
di in r " singleton dummy variable (dummy with one 1 and N-1 0s or vice versa)"
di in smcl _c "{help `helpfile'##fwl:fwl}"
di in r " option may address problem."
}
* Display endog option: endogeneity test statistic
if e(estat) != . {
* If stat = 0 then warn, otherwise output
if e(estat) > 0 {
di in gr "-endog- option:"
di in smcl _c "{help `helpfile'##endogtest:Endogeneity test}"
di in gr _c " of endogenous regressors: "
di in ye _col(71) %8.3f e(estat)
di in gr _col(52) "Chi-sq(" in ye e(estatdf) /*
*/ in gr ") P-val = " in ye _col(73) %6.4f e(estatp)
di in gr "Regressors tested: " _c
Disp `e(elist)', _col(23)
}
if e(estat) == 0 {
di in gr _n "Collinearity/identification problems in restricted equation:"
di in gr " Endogeneity test statistic not calculated for -endog- option"
}
}
di in smcl in gr "{hline 78}"
* Display AR overid statistic if LIML and not robust
if "`e(model)'" == "liml" & "`e(vcetype)'" ~= "Robust" & "`e(kernel)'" == "" {
if "`e(instd)'" != "" {
di in smcl _c "{help `helpfile'##liml:Anderson-Rubin statistic}"
di in gr _c " (overidentification test of all instruments):"
}
else {
di in smcl _c "{help `helpfile'##liml:Anderson-Rubin statistic}"
di in gr _c " (LR test of excluded instruments):"
}
di in ye _col(72) %7.3f e(arubin)
if e(rankxx) < e(rankzz) {
di in gr _col(52) "Chi-sq(" in ye e(arubindf) /*
*/ in gr ") P-val = " in ye _col(73) %6.4f e(arubinp)
}
else {
di in gr _col(50) "(equation exactly identified)"
}
di in smcl in gr "{hline 78}"
}
}
* Display 2nd footer with variable lists
if "`nofooter'"=="" {
* Warn about dropped instruments if any
* (Re-)calculate number of user-supplied instruments
local iv1_ct : word count `e(insts)'
local iv1_ct = `iv1_ct' + `e(cons)'
if `iv1_ct' > e(rankzz) {
di in gr "Collinearities detected among instruments: " _c
di in gr `iv1_ct'-e(rankzz) " instrument(s) dropped"
}
if "`e(collin)'`e(dups)'`e(fwlcons)'" != "" {
* If collinearities, duplicates or fwl, abbreviated varlists saved with a 1 at the end
local one "1"
}
if "`e(instd)'" != "" {
di in gr "Instrumented:" _c
Disp `e(instd`one')', _col(23)
}
if "`e(inexog)'" != "" {
di in gr "Included instruments:" _c
Disp `e(inexog`one')', _col(23)
}
if "`e(exexog)'" != "" {
di in gr "Excluded instruments:" _c
Disp `e(exexog`one')', _col(23)
}
if "`e(fwlcons)'" != "" {
if e(fwlcons) {
local fwl "`e(fwl`one')' _cons"
}
else {
local fwl "`e(fwl`one')'"
}
di in smcl _c "{help `helpfile'##fwl:Partialled-out (FWL)}"
di in gr ":" _c
Disp `fwl', _col(23)
di in gr _col(23) "nb: variable counts and small-sample adjustments"
di in gr _col(23) "do not include partialled-out variables."
}
if "`e(dups)'" != "" {
di in gr "Duplicates:" _c
Disp `e(dups)', _col(23)
}
if "`e(collin)'" != "" {
di in gr "Dropped collinear:" _c
Disp `e(collin)', _col(23)
}
if "`e(ecollin)'" != "" {
di in gr "Reclassified as exog:" _c
Disp `e(ecollin)', _col(23)
}
di in smcl in gr "{hline 78}"
}
end
**************************************************************************************
program define DispRF
version 8.2
local eqname "`e(rfeq)'"
local depvar "`e(depvar)'"
local strlen : length local depvar
local strlen = `strlen'+25
di
di in gr "Reduced-form regression: `e(depvar)'"
di in smcl in gr "{hline `strlen'}"
capture estimates replay `eqname'
if "`eqname'"=="" | _rc != 0 {
di in ye "Unable to display reduced-form regression of `e(depvar)'."
di in ye "There may be insufficient room to store results using -estimates store-."
di in ye "Try dropping one or more estimation results using -estimates drop-."
di
}
else {
estimates replay `eqname', noheader
di
}
end
program define DispFirst
version 8.2
args helpfile
tempname firstmat ivest sheapr2 pr2 F df df_r pvalue
mat `firstmat'=e(first)
if `firstmat'[1,1] == . {
di
di in ye "Unable to display first-stage estimates; macro e(first) is missing"
exit
}
di in gr _newline "First-stage regressions"
di in smcl in gr "{hline 23}"
di
local endo1 : colnames(`firstmat')
local nrvars : word count `endo1'
local firsteqs "`e(firsteqs)'"
local nreqs : word count `firsteqs'
if `nreqs' < `nrvars' {
di in ye "Unable to display all first-stage regressions."
di in ye "There may be insufficient room to store results using -estimates store-."
di in ye "Try dropping one or more estimation results using -estimates drop-."
di
}
local robust "`e(vcetype)'"
local cluster "`e(clustvar)'"
local kernel "`e(kernel)'"
foreach eqname of local firsteqs {
_estimates hold `ivest'
capture estimates restore `eqname'
if _rc != 0 {
di
di in ye "Unable to list stored estimation `eqname'."
di in ye "There may be insufficient room to store results using -estimates store-."
di in ye "Try dropping one or more estimation results using -estimates drop-."
di
}
else {
local vn "`e(depvar)'"
di in gr "First-stage regression of `vn':"
estimates replay `eqname', noheader
mat `sheapr2' =`firstmat'["sheapr2","`vn'"]
mat `pr2' =`firstmat'["pr2","`vn'"]
mat `F' =`firstmat'["F","`vn'"]
mat `df' =`firstmat'["df","`vn'"]
mat `df_r' =`firstmat'["df_r","`vn'"]
mat `pvalue' =`firstmat'["pvalue","`vn'"]
di in smcl _c "{help `helpfile'##partialr2:Partial R-squared}"
di in gr " of excluded instruments: " _c
di in ye %8.4f `pr2'[1,1]
di in gr "Test of excluded instruments:"
di in gr " F(" %3.0f `df'[1,1] "," %6.0f `df_r'[1,1] ") = " in ye %8.2f `F'[1,1]
di in gr " Prob > F = " in ye %8.4f `pvalue'[1,1]
di
}
_estimates unhold `ivest'
}
end
program define DispStored
args saverf savefirst helpfile
version 8.2
if "`saverf'" != "" {
local eqlist "`e(rfeq)'"
}
if "`savefirst'" != "" {
local eqlist "`eqlist' `e(firsteqs)'"
}
local eqlist : list retokenize eqlist
di in gr _newline "Stored estimation results"
di in smcl in gr "{hline 25}" _c
capture estimates dir `eqlist'
if "`eqlist'" != "" & _rc == 0 {
* Estimates exist and can be listed
estimates dir `eqlist'
}
else if "`eqlist'" != "" & _rc != 0 {
di
di in ye "Unable to list stored estimations."
di in ye "There may be insufficient room to store results using -estimates store-."
di in ye "Try dropping one or more estimation results using -estimates drop-."
di
}
end
program define DispFFirst
version 8.2
args helpfile
tempname firstmat sheapr2 pr2 pr2F pr2p
mat `firstmat'=e(first)
if `firstmat'[1,1] == . {
di
di in ye "Unable to display summary of first-stage estimates; macro e(first) is missing"
exit
}
local endo : colnames(`firstmat')
local nrvars : word count `endo'
local robust "`e(vcetype)'"
local cluster "`e(clustvar)'"
local kernel "`e(kernel)'"
local efirsteqs "`e(firsteqs)'"
di
di in gr _newline "Summary results for first-stage regressions"
di in smcl in gr "{hline 43}"
di
di in gr _c "Variable |"
di in smcl _c _col(15) "{help `helpfile'##partialr2:Shea Partial R2}"
di in gr _c _col(31) "|"
di in smcl _c _col(35) "{help `helpfile'##partialr2:Partial R2}"
di in gr _c _col(49) "|"
di in smcl _c _col(52) "{help `helpfile'##partialr2:F}"
di in gr _c _col(53) "("
di in ye _col(54) %3.0f `firstmat'[4,1] in gr "," in ye %6.0f `firstmat'[5,1] in gr ") P-value"
local i = 1
while `i' <= `nrvars' {
local vn : word `i' of `endo'
local vnlen : length local vn
if `vnlen' > 12 {
local vn : piece 1 12 of "`vn'"
}
scalar `sheapr2'=`firstmat'[1,`i']
scalar `pr2'=`firstmat'[2,`i']
scalar `pr2F'=`firstmat'[3,`i']
scalar `pr2p'=`firstmat'[6,`i']
di in y %-12s "`vn'" _col(13) in gr "|" _col(17) in y %8.4f `sheapr2' _col(31) in gr "|" /*
*/ _col(35) in y %8.4f `pr2' _col(49) in gr "|" /*
*/ _col(53) in y %8.2f `pr2F' _col(67) %8.4f `pr2p'
local i = `i' + 1
}
di
if "`robust'`cluster'" != "" {
if "`cluster'" != "" {
local rtype "cluster-robust"
}
else if "`kernel'" != "" {
local rtype "heteroskedasticity and autocorrelation-robust"
}
else {
local rtype "heteroskedasticity-robust"
}
}
else if "`kernel'" != "" {
local rtype "autocorrelation-robust"
}
if "`robust'`cluster'`kernel'" != "" {
di in gr "NB: first-stage F-stat `rtype'"
di
}
tempname iddf idstat idp cdchi2 cdchi2p cdf
scalar `iddf'=e(iddf)
scalar `idstat'=e(idstat)
scalar `idp'=e(idp)
scalar `cdchi2'=e(cdchi2)
scalar `cdchi2p'=e(cdchi2p)
scalar `cdf'=e(cdf)
di in smcl "{help `helpfile'##s_first:Underidentification tests}"
di in gr "Ho: matrix of reduced form coefficients has rank=K-1 (underidentified)"
di in gr "Ha: matrix has rank=K (identified)"
di in gr _col(50) "Chi-sq(" in ye `iddf' in gr ")" _col(65) "P-value"
di in ye "Anderson canon. corr. -N*ln(1-CCEV) LR stat." _col(49) %8.2f `idstat' _col(65) %7.4f `idp'
di in ye "Cragg-Donald N*CDEV statistic" _col(49) %8.2f `cdchi2' _col(65) %7.4f `cdchi2p'
if "`robust'`cluster'`kernel'" != "" & e(endog_ct)==1 {
tempname rchi2 rchi2p
* Robust chi2 recreated from robust F and dofs of non-robust C-D chi2 and F
scalar `rchi2'=`pr2F'*`cdchi2'/`cdf'
scalar `rchi2p'=chiprob(`iddf',`rchi2')
di in ye "Robust chi-square statistic" _col(49) %8.2f `rchi2' _col(65) %7.4f `rchi2p'
}
di
di in smcl "{help `helpfile'##s_first:Weak identification tests}"
di in gr "Ho: equation is weakly identified"
di in ye "Cragg-Donald (N-L)*CDEV/L1 F-statistic" _col(49) %8.2f `cdf'
if "`robust'`cluster'`kernel'" != "" & e(endog_ct)==1 {
di in ye "Robust F-statistic" _col(49) %8.2f `pr2F'
}
di in gr "See main output for Cragg-Donald weak id test critical values"
di
if "`robust'`cluster'`kernel'" != "" {
di in gr "NB: Anderson and Cragg-Donald under- and weak identification stats not robust"
if "`robust'`cluster'`kernel'" != "" & e(endog_ct)==1 {
di in gr " Robust identification stats `rtype'"
}
}
di
tempname arf arfp archi2 archi2p ardf ardf_r
tempname sstat sstatp sstatdf
di in smcl "{help `helpfile'##wirobust:Weak-instrument-robust inference}"
di in gr "Tests of joint significance of endogenous regressors B1 in main equation"
di in gr "Ho: B1=0 and overidentifying restrictions are valid"
* Needs to be small so that adjusted dof is reflected in F stat
scalar `arf'=e(arf)
scalar `arfp'=e(arfp)
scalar `archi2'=e(archi2)
scalar `archi2p'=e(archi2p)
scalar `ardf'=e(ardf)
scalar `ardf_r'=e(ardf_r)
scalar `sstat'=e(sstat)
scalar `sstatp'=e(sstatp)
scalar `sstatdf'=e(sstatdf)
di in ye _c "Anderson-Rubin test"
di in gr _col(30) "F(" in ye `ardf' in gr "," in ye `ardf_r' in gr ")=" /*
*/ _col(40) in ye %-7.2f `arf' _col(50) in gr "P-val=" in ye %6.4f `arfp'
di in ye _c "Anderson-Rubin test"
di in gr _col(30) "Chi-sq(" in ye `ardf' in gr ")=" /*
*/ _col(40) in ye %-7.2f `archi2' _col(50) in gr "P-val=" in ye %6.4f `archi2p'
di in ye _c "Stock-Wright S statistic"
di in gr _col(30) "Chi-sq(" in ye `sstatdf' in gr ")=" /*
*/ _col(40) in ye %-7.2f `sstat' _col(50) in gr "P-val=" in ye %6.4f `sstatp'
if "`robust'`cluster'`kernel'" != "" {
di in gr "NB: Test statistics `rtype'"
}
di
if "`cluster'" != "" {
di in gr "Number of clusters N_clust = " in ye %10.0f e(N_clust)
}
di in gr "Number of observations N = " in ye %10.0f e(N)
di in gr "Number of regressors K = " in ye %10.0f e(rankxx)
di in gr "Number of instruments L = " in ye %10.0f e(rankzz)
di in gr "Number of excluded instruments L1 = " in ye %10.0f e(ardf)
di
end
* Performs first-stage regressions
program define doFirst, rclass
version 8.2
args endog /* variable list (including depvar)
*/ inexog /* list of included exogenous
*/ exexog /* list of excluded exogenous
*/ touse /* touse sample
*/ weight /* full weight expression w/ []
*/ nocons /*
*/ robust /*
*/ clopt /*
*/ bwopt /*
*/ kernopt /*
*/ savefprefix /*
*/ dofmopt /*
*/ sw /*
*/ swpsd /*
*/ ivreg2_cmd
tokenize `endog'
tempname statmat statmat1
local i 1
while "``i''" != "" {
capture `ivreg2_cmd' ``i'' `inexog' `exexog' `weight' /*
*/ if `touse', `nocons' `robust' `clopt' `bwopt' `kernopt' `dofmopt' `sw' `swpsd' small
if _rc ~= 0 {
* First-stage regression failed
di in ye "Unable to estimate first-stage regression of ``i''"
if _rc == 506 {
di in ye " var-cov matrix of first-stage regression of ``i'' not positive-definite"
}
}
else {
* First-stage regression successful
* Check if there is enough room to save results; leave one free. Allow for overwriting.
* Max is 20-1=19 for Stata 9.0 and earlier, 300-1=299 for Stata 9.1+
if c(stata_version) < 9.1 {
local maxest=19
}
else {
local maxest=299
}
local vn "``i''"
local plen : length local savefprefix
local vlen : length local vn
if `plen'+`vlen' > 27 {
local vlen=27-`plen'
local vn : permname `vn', length(`vlen')
* Must create a variable so that permname doesn't reuse it
gen `vn'=0
local dropvn "`dropvn' `vn'"
}
local eqname "`savefprefix'`vn'"
local eqname : subinstr local eqname "." "_"
qui estimates dir
local est_list "`r(names)'"
Subtract est_list : "`est_list'" "`eqname'"
local est_ct : word count `est_list'
if `est_ct' < `maxest' {
capture est store `eqname', title("First-stage regression: ``i''")
if _rc == 0 {
local firsteqs "`firsteqs' `eqname'"
}
}
else {
di
di in ye "Unable to store first-stage regression of ``i''."
di in ye "There may be insufficient room to store results using -estimates store-."
di in ye "Try dropping one or more estimation results using -estimates drop-."
di
}
tempname rssall rssinc pr2 F p
scalar `rssall'=e(rss)
quietly test `exexog'
scalar `F'=r(F)
scalar `p'=r(p)
local df=r(df)
local df_r=r(df_r)
* 1st stage regression without excluded exogenous
* Use regress since need only RSS and handles all cases, including perverse ones (e.g. no regressors)
qui regress ``i'' `inexog' `weight' /*
*/ if `touse', `nocons'
*/ if `touse', `nocons' `robust' `clopt' `bwopt' `kernopt' `dofmopt' `sw' `swpsd' small
scalar `rssinc'=e(rss)
* NB: uncentered R2 for main regression is 1-rssall/yy; for restricted is 1-rssinc/yy;
* squared semipartial correlation=(rssinc-rssall)/yy=diff of 2 R2s
* Squared partial correlation (="partialled-out R2")
scalar `pr2'=(`rssinc'-`rssall')/`rssinc'
* End of first-stage successful block
}
capture {
mat `statmat1' = (`pr2' \ `F' \ `df' \ `df_r' \ `p')
mat colname `statmat1' = ``i''
if `i'==1 {
mat `statmat'=`statmat1'
}
else {
mat `statmat' = `statmat' , `statmat1'
}
}
local i = `i' + 1
}
* Drop any temporarily-created permname variables
if trim("`dropvn'")~="" {
foreach vn of varlist `dropvn' {
capture drop `vn'
}
}
capture mat rowname `statmat' = pr2 F df df_r pvalue
if _rc==0 {
return matrix firstmat `statmat'
}
return local firsteqs "`firsteqs'"
end
program define doRF, rclass
version 8.2
args lhs /*
*/ inexog /* list of included exogenous
*/ exexog /* list of excluded exogenous
*/ touse /* touse sample
*/ weight /* full weight expression w/ []
*/ nocons /*
*/ robust /*
*/ clopt /*
*/ bwopt /*
*/ kernopt /*
*/ saverfprefix /*
*/ dofminus /*
*/ sw /*
*/ swpsd /*
*/ ivreg2_cmd
* Anderson-Rubin test of signif of endog regressors (Bo=0)
* In case ivreg2 called with adjusted dof, first stage should adjust dof as well
tempname arf arfp archi2 archi2p ardf ardf_r tempest
capture _estimates hold `tempest'
if _rc==1000 {
di as err "ivreg2 internal error - no room to save temporary estimation results"
di as err "Please drop one or more estimation results using -estimates drop-"
exit 1000
}
* Needs to be small so that adjusted dof is reflected in F stat
qui `ivreg2_cmd' `lhs' `inexog' `exexog' `weight' if `touse', /*
*/ small `nocons' dofminus(`dofminus') `robust' `clopt' `bwopt' `kernopt' `sw' `swpsd'
qui test `exexog'
scalar `arf'=r(F)
scalar `arfp'=r(p)
scalar `ardf'=r(df)
scalar `ardf_r'=r(df_r)
if "`clopt'"=="" {
scalar `archi2'=`arf'*`ardf'*(e(N)-`dofminus')/(e(N)-e(rankxx)-`dofminus')
}
else {
scalar `archi2'=`arf'*`ardf'*e(N_clust)/r(df_r)*(e(N)-1)/(e(N)-e(rankxx))
}
scalar `archi2p'=chiprob(`ardf',`archi2')
* Check if there is enough room to save results; leave one free. Allow for overwriting.
* Max is 20-1=19 for Stata 9.0 and earlier, 300-1=299 for Stata 9.1+
if c(stata_version) < 9.1 {
local maxest=19
}
else {
local maxest=299
}
local vn "`lhs'"
local plen : length local saverfprefix
local vlen : length local lhs
if `plen'+`vlen' > 27 {
local vlen=27-`plen'
local vn : permname `vn', length(`vlen')
}
local eqname "`saverfprefix'`vn'"
local eqname : subinstr local eqname "." "_"
qui estimates dir
local est_list "`r(names)'"
Subtract est_list : "`est_list'" "`eqname'"
local est_ct : word count `est_list'
if `est_ct' < `maxest' {
capture est store `eqname', title("Reduced-form regression: `lhs'")
return local rfeq "`eqname'"
}
else {
di
di in ye "Unable to store reduced-form regression of `lhs'."
di in ye "There may be insufficient room to store results using -estimates store-."
di in ye "Try dropping one or more estimation results using -estimates drop-."
di
}
_estimates unhold `tempest'
return scalar arf=`arf'
return scalar arfp=`arfp'
return scalar ardf=`ardf'
return scalar ardf_r=`ardf_r'
return scalar archi2=`archi2'
return scalar archi2p=`archi2p'
end
**************************************************************************************
program define IsStop, sclass
/* sic, must do tests one-at-a-time,
* 0, may be very large */
version 8.2
if `"`0'"' == "[" {
sret local stop 1
exit
}
if `"`0'"' == "," {
sret local stop 1
exit
}
if `"`0'"' == "if" {
sret local stop 1
exit
}
* per official ivreg 5.1.3
if substr(`"`0'"',1,3) == "if(" {
sret local stop 1
exit
}
if `"`0'"' == "in" {
sret local stop 1
exit
}
if `"`0'"' == "" {
sret local stop 1
exit
}
else sret local stop 0
end
program define Disp
version 8.2
syntax [anything] [, _col(integer 15) ]
local len = 80-`_col'+1
local piece : piece 1 `len' of `"`anything'"'
local i 1
while "`piece'" != "" {
di in gr _col(`_col') "`first'`piece'"
local i = `i' + 1
local piece : piece `i' `len' of `"`anything'"'
}
if `i'==1 {
di
}
end
* Remove all tokens in dirt from full
* Returns "cleaned" full list in cleaned
program define Subtract /* <cleaned> : <full> <dirt> */
version 8.2
args cleaned /* macro name to hold cleaned list
*/ colon /* ":"
*/ full /* list to be cleaned
*/ dirt /* tokens to be cleaned from full */
tokenize `dirt'
local i 1
while "``i''" != "" {
local full : subinstr local full "``i''" "", word all
local i = `i' + 1
}
tokenize `full' /* cleans up extra spaces */
c_local `cleaned' `*'
end
program define vecsort /* Also clears col/row names */
version 8.2
args vmat
tempname hold
mat `vmat'=`vmat'+J(rowsof(`vmat'),colsof(`vmat'),0)
local lastcol = colsof(`vmat')
local i 1
while `i' < `lastcol' {
if `vmat'[1,`i'] > `vmat'[1,`i'+1] {
scalar `hold' = `vmat'[1,`i']
mat `vmat'[1,`i'] = `vmat'[1,`i'+1]
mat `vmat'[1,`i'+1] = `hold'
local i = 1
}
else {
local i = `i' + 1
}
}
end
program define matsort
version 8.2
args vmat names
tempname hold
foreach vn in `names' {
mat `hold'=nullmat(`hold'), `vmat'[1...,"`vn'"]
}
mat `vmat'=`hold'
mat drop `hold'
foreach vn in `names' {
mat `hold'=nullmat(`hold') \ `vmat'["`vn'",1...]
}
mat `vmat'=`hold'
end
program define cdsy, rclass
version 8.2
syntax , type(string) k2(integer) nendog(integer)
* type() can be ivbias5 (k2<=100, nendog<=3)
* ivbias10 (ditto)
* ivbias20 (ditto)
* ivbias30 (ditto)
* ivsize10 (k2<=100, nendog<=2)
* ivsize15 (ditto)
* ivsize20 (ditto)
* ivsize25 (ditto)
* fullrel5 (ditto)
* fullrel10 (ditto)
* fullrel20 (ditto)
* fullrel30 (ditto)
* fullmax5 (ditto)
* fullmax10 (ditto)
* fullmax20 (ditto)
* fullmax30 (ditto)
* limlsize10 (ditto)
* limlsize15 (ditto)
* limlsize20 (ditto)
* limlsize25 (ditto)
tempname temp cv
* Initialize critical value as MV
scalar `cv'=.
if "`type'"=="ivbias5" {
matrix input `temp' = ( /*
*/ . , . , . \ /*
*/ . , . , . \ /*
*/ 13.91 , . , . \ /*
*/ 16.85 , 11.04 , . \ /*
*/ 18.37 , 13.97 , 9.53 \ /*
*/ 19.28 , 15.72 , 12.20 \ /*
*/ 19.86 , 16.88 , 13.95 \ /*
*/ 20.25 , 17.70 , 15.18 \ /*
*/ 20.53 , 18.30 , 16.10 \ /*
*/ 20.74 , 18.76 , 16.80 \ /*
*/ 20.90 , 19.12 , 17.35 \ /*
*/ 21.01 , 19.40 , 17.80 \ /*
*/ 21.10 , 19.64 , 18.17 \ /*
*/ 21.18 , 19.83 , 18.47 \ /*
*/ 21.23 , 19.98 , 18.73 \ /*
*/ 21.28 , 20.12 , 18.94 \ /*
*/ 21.31 , 20.23 , 19.13 \ /*
*/ 21.34 , 20.33 , 19.29 \ /*
*/ 21.36 , 20.41 , 19.44 \ /*
*/ 21.38 , 20.48 , 19.56 \ /*
*/ 21.39 , 20.54 , 19.67 \ /*
*/ 21.40 , 20.60 , 19.77 \ /*
*/ 21.41 , 20.65 , 19.86 \ /*
*/ 21.41 , 20.69 , 19.94 \ /*
*/ 21.42 , 20.73 , 20.01 \ /*
*/ 21.42 , 20.76 , 20.07 \ /*
*/ 21.42 , 20.79 , 20.13 \ /*
*/ 21.42 , 20.82 , 20.18 \ /*
*/ 21.42 , 20.84 , 20.23 \ /*
*/ 21.42 , 20.86 , 20.27 \ /*
*/ 21.41 , 20.88 , 20.31 \ /*
*/ 21.41 , 20.90 , 20.35 \ /*
*/ 21.41 , 20.91 , 20.38 \ /*
*/ 21.40 , 20.93 , 20.41 \ /*
*/ 21.40 , 20.94 , 20.44 \ /*
*/ 21.39 , 20.95 , 20.47 \ /*
*/ 21.39 , 20.96 , 20.49 \ /*
*/ 21.38 , 20.97 , 20.51 \ /*
*/ 21.38 , 20.98 , 20.54 \ /*
*/ 21.37 , 20.99 , 20.56 \ /*
*/ 21.37 , 20.99 , 20.57 \ /*
*/ 21.36 , 21.00 , 20.59 \ /*
*/ 21.35 , 21.00 , 20.61 \ /*
*/ 21.35 , 21.01 , 20.62 \ /*
*/ 21.34 , 21.01 , 20.64 \ /*
*/ 21.34 , 21.02 , 20.65 \ /*
*/ 21.33 , 21.02 , 20.66 \ /*
*/ 21.32 , 21.02 , 20.67 \ /*
*/ 21.32 , 21.03 , 20.68 \ /*
*/ 21.31 , 21.03 , 20.69 \ /*
*/ 21.31 , 21.03 , 20.70 \ /*
*/ 21.30 , 21.03 , 20.71 \ /*
*/ 21.30 , 21.03 , 20.72 \ /*
*/ 21.29 , 21.03 , 20.73 \ /*
*/ 21.28 , 21.03 , 20.73 \ /*
*/ 21.28 , 21.04 , 20.74 \ /*
*/ 21.27 , 21.04 , 20.75 \ /*
*/ 21.27 , 21.04 , 20.75 \ /*
*/ 21.26 , 21.04 , 20.76 \ /*
*/ 21.26 , 21.04 , 20.76 \ /*
*/ 21.25 , 21.04 , 20.77 \ /*
*/ 21.24 , 21.04 , 20.77 \ /*
*/ 21.24 , 21.04 , 20.78 \ /*
*/ 21.23 , 21.04 , 20.78 \ /*
*/ 21.23 , 21.03 , 20.79 \ /*
*/ 21.22 , 21.03 , 20.79 \ /*
*/ 21.22 , 21.03 , 20.79 \ /*
*/ 21.21 , 21.03 , 20.80 \ /*
*/ 21.21 , 21.03 , 20.80 \ /*
*/ 21.20 , 21.03 , 20.80 \ /*
*/ 21.20 , 21.03 , 20.80 \ /*
*/ 21.19 , 21.03 , 20.81 \ /*
*/ 21.19 , 21.03 , 20.81 \ /*
*/ 21.18 , 21.03 , 20.81 \ /*
*/ 21.18 , 21.02 , 20.81 \ /*
*/ 21.17 , 21.02 , 20.82 \ /*
*/ 21.17 , 21.02 , 20.82 \ /*
*/ 21.16 , 21.02 , 20.82 \ /*
*/ 21.16 , 21.02 , 20.82 \ /*
*/ 21.15 , 21.02 , 20.82 \ /*
*/ 21.15 , 21.02 , 20.82 \ /*
*/ 21.15 , 21.02 , 20.83 \ /*
*/ 21.14 , 21.01 , 20.83 \ /*
*/ 21.14 , 21.01 , 20.83 \ /*
*/ 21.13 , 21.01 , 20.83 \ /*
*/ 21.13 , 21.01 , 20.83 \ /*
*/ 21.12 , 21.01 , 20.84 \ /*
*/ 21.12 , 21.01 , 20.84 \ /*
*/ 21.11 , 21.01 , 20.84 \ /*
*/ 21.11 , 21.01 , 20.84 \ /*
*/ 21.10 , 21.00 , 20.84 \ /*
*/ 21.10 , 21.00 , 20.84 \ /*
*/ 21.09 , 21.00 , 20.85 \ /*
*/ 21.09 , 21.00 , 20.85 \ /*
*/ 21.08 , 21.00 , 20.85 \ /*
*/ 21.08 , 21.00 , 20.85 \ /*
*/ 21.07 , 21.00 , 20.85 \ /*
*/ 21.07 , 20.99 , 20.86 \ /*
*/ 21.06 , 20.99 , 20.86 \ /*
*/ 21.06 , 20.99 , 20.86 )
if `k2'<=100 & `nendog'<=3 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="ivbias10" {
matrix input `temp' = /*
*/ (.,.,. \ /*
*/ .,.,. \ /*
*/ 9.08,.,. \ /*
*/ 10.27,7.56,. \ /*
*/ 10.83,8.78,6.61 \ /*
*/ 11.12,9.48,7.77 \ /*
*/ 11.29,9.92,8.5 \ /*
*/ 11.39,10.22,9.01 \ /*
*/ 11.46,10.43,9.37 \ /*
*/ 11.49,10.58,9.64 \ /*
*/ 11.51,10.69,9.85 \ /*
*/ 11.52,10.78,10.01 \ /*
*/ 11.52,10.84,10.14 \ /*
*/ 11.52,10.89,10.25 \ /*
*/ 11.51,10.93,10.33 \ /*
*/ 11.5,10.96,10.41 \ /*
*/ 11.49,10.99,10.47 \ /*
*/ 11.48,11,10.52 \ /*
*/ 11.46,11.02,10.56 \ /*
*/ 11.45,11.03,10.6 \ /*
*/ 11.44,11.04,10.63 \ /*
*/ 11.42,11.05,10.65 \ /*
*/ 11.41,11.05,10.68 \ /*
*/ 11.4,11.05,10.7 \ /*
*/ 11.38,11.06,10.71 \ /*
*/ 11.37,11.06,10.73 \ /*
*/ 11.36,11.06,10.74 \ /*
*/ 11.34,11.05,10.75 \ /*
*/ 11.33,11.05,10.76 \ /*
*/ 11.32,11.05,10.77 \ /*
*/ 11.3,11.05,10.78 \ /*
*/ 11.29,11.05,10.79 \ /*
*/ 11.28,11.04,10.79 \ /*
*/ 11.27,11.04,10.8 \ /*
*/ 11.26,11.04,10.8 \ /*
*/ 11.25,11.03,10.8 \ /*
*/ 11.24,11.03,10.81 \ /*
*/ 11.23,11.02,10.81 \ /*
*/ 11.22,11.02,10.81 \ /*
*/ 11.21,11.02,10.81 \ /*
*/ 11.2,11.01,10.81 \ /*
*/ 11.19,11.01,10.81 \ /*
*/ 11.18,11,10.81 \ /*
*/ 11.17,11,10.81 \ /*
*/ 11.16,10.99,10.81 \ /*
*/ 11.15,10.99,10.81 \ /*
*/ 11.14,10.98,10.81 \ /*
*/ 11.13,10.98,10.81 \ /*
*/ 11.13,10.98,10.81 \ /*
*/ 11.12,10.97,10.81 \ /*
*/ 11.11,10.97,10.81 \ /*
*/ 11.1,10.96,10.81 \ /*
*/ 11.1,10.96,10.81 \ /*
*/ 11.09,10.95,10.81 \ /*
*/ 11.08,10.95,10.81 \ /*
*/ 11.07,10.94,10.8 \ /*
*/ 11.07,10.94,10.8 \ /*
*/ 11.06,10.94,10.8 \ /*
*/ 11.05,10.93,10.8 \ /*
*/ 11.05,10.93,10.8 \ /*
*/ 11.04,10.92,10.8 \ /*
*/ 11.03,10.92,10.79 \ /*
*/ 11.03,10.92,10.79 \ /*
*/ 11.02,10.91,10.79 \ /*
*/ 11.02,10.91,10.79 \ /*
*/ 11.01,10.9,10.79 \ /*
*/ 11,10.9,10.79 \ /*
*/ 11,10.9,10.78 \ /*
*/ 10.99,10.89,10.78 \ /*
*/ 10.99,10.89,10.78 \ /*
*/ 10.98,10.89,10.78 \ /*
*/ 10.98,10.88,10.78 \ /*
*/ 10.97,10.88,10.77 \ /*
*/ 10.97,10.88,10.77 \ /*
*/ 10.96,10.87,10.77 \ /*
*/ 10.96,10.87,10.77 \ /*
*/ 10.95,10.86,10.77 \ /*
*/ 10.95,10.86,10.76 \ /*
*/ 10.94,10.86,10.76 \ /*
*/ 10.94,10.85,10.76 \ /*
*/ 10.93,10.85,10.76 \ /*
*/ 10.93,10.85,10.76 \ /*
*/ 10.92,10.84,10.75 \ /*
*/ 10.92,10.84,10.75 \ /*
*/ 10.91,10.84,10.75 \ /*
*/ 10.91,10.84,10.75 \ /*
*/ 10.91,10.83,10.75 \ /*
*/ 10.9,10.83,10.74 \ /*
*/ 10.9,10.83,10.74 \ /*
*/ 10.89,10.82,10.74 \ /*
*/ 10.89,10.82,10.74 \ /*
*/ 10.89,10.82,10.74 \ /*
*/ 10.88,10.81,10.74 \ /*
*/ 10.88,10.81,10.73 \ /*
*/ 10.87,10.81,10.73 \ /*
*/ 10.87,10.81,10.73 \ /*
*/ 10.87,10.8,10.73 \ /*
*/ 10.86,10.8,10.73 \ /*
*/ 10.86,10.8,10.72 \ /*
*/ 10.86,10.8,10.72)
if `k2'<=100 & `nendog'<=3 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="ivbias20" {
matrix input `temp' = ( /*
*/ . , . , . \ /*
*/ . , . , . \ /*
*/ 6.46 , . , . \ /*
*/ 6.71 , 5.57 , . \ /*
*/ 6.77 , 5.91 , 4.99 \ /*
*/ 6.76 , 6.08 , 5.35 \ /*
*/ 6.73 , 6.16 , 5.56 \ /*
*/ 6.69 , 6.20 , 5.69 \ /*
*/ 6.65 , 6.22 , 5.78 \ /*
*/ 6.61 , 6.23 , 5.83 \ /*
*/ 6.56 , 6.23 , 5.87 \ /*
*/ 6.53 , 6.22 , 5.90 \ /*
*/ 6.49 , 6.21 , 5.92 \ /*
*/ 6.45 , 6.20 , 5.93 \ /*
*/ 6.42 , 6.19 , 5.94 \ /*
*/ 6.39 , 6.17 , 5.94 \ /*
*/ 6.36 , 6.16 , 5.94 \ /*
*/ 6.33 , 6.14 , 5.94 \ /*
*/ 6.31 , 6.13 , 5.94 \ /*
*/ 6.28 , 6.11 , 5.93 \ /*
*/ 6.26 , 6.10 , 5.93 \ /*
*/ 6.24 , 6.08 , 5.92 \ /*
*/ 6.22 , 6.07 , 5.92 \ /*
*/ 6.20 , 6.06 , 5.91 \ /*
*/ 6.18 , 6.05 , 5.90 \ /*
*/ 6.16 , 6.03 , 5.90 \ /*
*/ 6.14 , 6.02 , 5.89 \ /*
*/ 6.13 , 6.01 , 5.88 \ /*
*/ 6.11 , 6.00 , 5.88 \ /*
*/ 6.09 , 5.99 , 5.87 \ /*
*/ 6.08 , 5.98 , 5.87 \ /*
*/ 6.07 , 5.97 , 5.86 \ /*
*/ 6.05 , 5.96 , 5.85 \ /*
*/ 6.04 , 5.95 , 5.85 \ /*
*/ 6.03 , 5.94 , 5.84 \ /*
*/ 6.01 , 5.93 , 5.83 \ /*
*/ 6.00 , 5.92 , 5.83 \ /*
*/ 5.99 , 5.91 , 5.82 \ /*
*/ 5.98 , 5.90 , 5.82 \ /*
*/ 5.97 , 5.89 , 5.81 \ /*
*/ 5.96 , 5.89 , 5.80 \ /*
*/ 5.95 , 5.88 , 5.80 \ /*
*/ 5.94 , 5.87 , 5.79 \ /*
*/ 5.93 , 5.86 , 5.79 \ /*
*/ 5.92 , 5.86 , 5.78 \ /*
*/ 5.91 , 5.85 , 5.78 \ /*
*/ 5.91 , 5.84 , 5.77 \ /*
*/ 5.90 , 5.83 , 5.77 \ /*
*/ 5.89 , 5.83 , 5.76 \ /*
*/ 5.88 , 5.82 , 5.76 \ /*
*/ 5.87 , 5.82 , 5.75 \ /*
*/ 5.87 , 5.81 , 5.75 \ /*
*/ 5.86 , 5.80 , 5.74 \ /*
*/ 5.85 , 5.80 , 5.74 \ /*
*/ 5.85 , 5.79 , 5.73 \ /*
*/ 5.84 , 5.79 , 5.73 \ /*
*/ 5.83 , 5.78 , 5.72 \ /*
*/ 5.83 , 5.78 , 5.72 \ /*
*/ 5.82 , 5.77 , 5.72 \ /*
*/ 5.81 , 5.77 , 5.71 \ /*
*/ 5.81 , 5.76 , 5.71 \ /*
*/ 5.80 , 5.76 , 5.70 \ /*
*/ 5.80 , 5.75 , 5.70 \ /*
*/ 5.79 , 5.75 , 5.70 \ /*
*/ 5.78 , 5.74 , 5.69 \ /*
*/ 5.78 , 5.74 , 5.69 \ /*
*/ 5.77 , 5.73 , 5.68 \ /*
*/ 5.77 , 5.73 , 5.68 \ /*
*/ 5.76 , 5.72 , 5.68 \ /*
*/ 5.76 , 5.72 , 5.67 \ /*
*/ 5.75 , 5.72 , 5.67 \ /*
*/ 5.75 , 5.71 , 5.67 \ /*
*/ 5.75 , 5.71 , 5.66 \ /*
*/ 5.74 , 5.70 , 5.66 \ /*
*/ 5.74 , 5.70 , 5.66 \ /*
*/ 5.73 , 5.70 , 5.65 \ /*
*/ 5.73 , 5.69 , 5.65 \ /*
*/ 5.72 , 5.69 , 5.65 \ /*
*/ 5.72 , 5.68 , 5.65 \ /*
*/ 5.71 , 5.68 , 5.64 \ /*
*/ 5.71 , 5.68 , 5.64 \ /*
*/ 5.71 , 5.67 , 5.64 \ /*
*/ 5.70 , 5.67 , 5.63 \ /*
*/ 5.70 , 5.67 , 5.63 \ /*
*/ 5.70 , 5.66 , 5.63 \ /*
*/ 5.69 , 5.66 , 5.62 \ /*
*/ 5.69 , 5.66 , 5.62 \ /*
*/ 5.68 , 5.65 , 5.62 \ /*
*/ 5.68 , 5.65 , 5.62 \ /*
*/ 5.68 , 5.65 , 5.61 \ /*
*/ 5.67 , 5.65 , 5.61 \ /*
*/ 5.67 , 5.64 , 5.61 \ /*
*/ 5.67 , 5.64 , 5.61 \ /*
*/ 5.66 , 5.64 , 5.60 \ /*
*/ 5.66 , 5.63 , 5.60 \ /*
*/ 5.66 , 5.63 , 5.60 \ /*
*/ 5.65 , 5.63 , 5.60 \ /*
*/ 5.65 , 5.63 , 5.59 \ /*
*/ 5.65 , 5.62 , 5.59 \ /*
*/ 5.65 , 5.62 , 5.59 )
if `k2'<=100 & `nendog'<=3 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="ivbias30" {
matrix input `temp' = ( /*
*/ . , . , . \ /*
*/ . , . , . \ /*
*/ 5.39 , . , . \ /*
*/ 5.34 , 4.73 , . \ /*
*/ 5.25 , 4.79 , 4.30 \ /*
*/ 5.15 , 4.78 , 4.40 \ /*
*/ 5.07 , 4.76 , 4.44 \ /*
*/ 4.99 , 4.73 , 4.46 \ /*
*/ 4.92 , 4.69 , 4.46 \ /*
*/ 4.86 , 4.66 , 4.45 \ /*
*/ 4.80 , 4.62 , 4.44 \ /*
*/ 4.75 , 4.59 , 4.42 \ /*
*/ 4.71 , 4.56 , 4.41 \ /*
*/ 4.67 , 4.53 , 4.39 \ /*
*/ 4.63 , 4.50 , 4.37 \ /*
*/ 4.59 , 4.48 , 4.36 \ /*
*/ 4.56 , 4.45 , 4.34 \ /*
*/ 4.53 , 4.43 , 4.32 \ /*
*/ 4.51 , 4.41 , 4.31 \ /*
*/ 4.48 , 4.39 , 4.29 \ /*
*/ 4.46 , 4.37 , 4.28 \ /*
*/ 4.43 , 4.35 , 4.27 \ /*
*/ 4.41 , 4.33 , 4.25 \ /*
*/ 4.39 , 4.32 , 4.24 \ /*
*/ 4.37 , 4.30 , 4.23 \ /*
*/ 4.35 , 4.29 , 4.21 \ /*
*/ 4.34 , 4.27 , 4.20 \ /*
*/ 4.32 , 4.26 , 4.19 \ /*
*/ 4.31 , 4.24 , 4.18 \ /*
*/ 4.29 , 4.23 , 4.17 \ /*
*/ 4.28 , 4.22 , 4.16 \ /*
*/ 4.26 , 4.21 , 4.15 \ /*
*/ 4.25 , 4.20 , 4.14 \ /*
*/ 4.24 , 4.19 , 4.13 \ /*
*/ 4.23 , 4.18 , 4.13 \ /*
*/ 4.22 , 4.17 , 4.12 \ /*
*/ 4.20 , 4.16 , 4.11 \ /*
*/ 4.19 , 4.15 , 4.10 \ /*
*/ 4.18 , 4.14 , 4.09 \ /*
*/ 4.17 , 4.13 , 4.09 \ /*
*/ 4.16 , 4.12 , 4.08 \ /*
*/ 4.15 , 4.11 , 4.07 \ /*
*/ 4.15 , 4.11 , 4.07 \ /*
*/ 4.14 , 4.10 , 4.06 \ /*
*/ 4.13 , 4.09 , 4.05 \ /*
*/ 4.12 , 4.08 , 4.05 \ /*
*/ 4.11 , 4.08 , 4.04 \ /*
*/ 4.11 , 4.07 , 4.03 \ /*
*/ 4.10 , 4.06 , 4.03 \ /*
*/ 4.09 , 4.06 , 4.02 \ /*
*/ 4.08 , 4.05 , 4.02 \ /*
*/ 4.08 , 4.05 , 4.01 \ /*
*/ 4.07 , 4.04 , 4.01 \ /*
*/ 4.06 , 4.03 , 4.00 \ /*
*/ 4.06 , 4.03 , 4.00 \ /*
*/ 4.05 , 4.02 , 3.99 \ /*
*/ 4.05 , 4.02 , 3.99 \ /*
*/ 4.04 , 4.01 , 3.98 \ /*
*/ 4.04 , 4.01 , 3.98 \ /*
*/ 4.03 , 4.00 , 3.97 \ /*
*/ 4.02 , 4.00 , 3.97 \ /*
*/ 4.02 , 3.99 , 3.96 \ /*
*/ 4.01 , 3.99 , 3.96 \ /*
*/ 4.01 , 3.98 , 3.96 \ /*
*/ 4.00 , 3.98 , 3.95 \ /*
*/ 4.00 , 3.97 , 3.95 \ /*
*/ 3.99 , 3.97 , 3.94 \ /*
*/ 3.99 , 3.97 , 3.94 \ /*
*/ 3.99 , 3.96 , 3.94 \ /*
*/ 3.98 , 3.96 , 3.93 \ /*
*/ 3.98 , 3.95 , 3.93 \ /*
*/ 3.97 , 3.95 , 3.93 \ /*
*/ 3.97 , 3.95 , 3.92 \ /*
*/ 3.96 , 3.94 , 3.92 \ /*
*/ 3.96 , 3.94 , 3.92 \ /*
*/ 3.96 , 3.93 , 3.91 \ /*
*/ 3.95 , 3.93 , 3.91 \ /*
*/ 3.95 , 3.93 , 3.91 \ /*
*/ 3.95 , 3.92 , 3.90 \ /*
*/ 3.94 , 3.92 , 3.90 \ /*
*/ 3.94 , 3.92 , 3.90 \ /*
*/ 3.93 , 3.91 , 3.89 \ /*
*/ 3.93 , 3.91 , 3.89 \ /*
*/ 3.93 , 3.91 , 3.89 \ /*
*/ 3.92 , 3.91 , 3.89 \ /*
*/ 3.92 , 3.90 , 3.88 \ /*
*/ 3.92 , 3.90 , 3.88 \ /*
*/ 3.91 , 3.90 , 3.88 \ /*
*/ 3.91 , 3.89 , 3.87 \ /*
*/ 3.91 , 3.89 , 3.87 \ /*
*/ 3.91 , 3.89 , 3.87 \ /*
*/ 3.90 , 3.89 , 3.87 \ /*
*/ 3.90 , 3.88 , 3.86 \ /*
*/ 3.90 , 3.88 , 3.86 \ /*
*/ 3.89 , 3.88 , 3.86 \ /*
*/ 3.89 , 3.87 , 3.86 \ /*
*/ 3.89 , 3.87 , 3.85 \ /*
*/ 3.89 , 3.87 , 3.85 \ /*
*/ 3.88 , 3.87 , 3.85 \ /*
*/ 3.88 , 3.86 , 3.85 )
if `k2'<=100 & `nendog'<=3 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="ivsize10" {
matrix input `temp' = /*
*/ (16.38,. \ /*
*/ 19.93,7.03 \ /*
*/ 22.3,13.43 \ /*
*/ 24.58,16.87 \ /*
*/ 26.87,19.45 \ /*
*/ 29.18,21.68 \ /*
*/ 31.5,23.72 \ /*
*/ 33.84,25.64 \ /*
*/ 36.19,27.51 \ /*
*/ 38.54,29.32 \ /*
*/ 40.9,31.11 \ /*
*/ 43.27,32.88 \ /*
*/ 45.64,34.62 \ /*
*/ 48.01,36.36 \ /*
*/ 50.39,38.08 \ /*
*/ 52.77,39.8 \ /*
*/ 55.15,41.51 \ /*
*/ 57.53,43.22 \ /*
*/ 59.92,44.92 \ /*
*/ 62.3,46.62 \ /*
*/ 64.69,48.31 \ /*
*/ 67.07,50.01 \ /*
*/ 69.46,51.7 \ /*
*/ 71.85,53.39 \ /*
*/ 74.24,55.07 \ /*
*/ 76.62,56.76 \ /*
*/ 79.01,58.45 \ /*
*/ 81.4,60.13 \ /*
*/ 83.79,61.82 \ /*
*/ 86.17,63.51 \ /*
*/ 88.56,65.19 \ /*
*/ 90.95,66.88 \ /*
*/ 93.33,68.56 \ /*
*/ 95.72,70.25 \ /*
*/ 98.11,71.94 \ /*
*/ 100.5,73.62 \ /*
*/ 102.88,75.31 \ /*
*/ 105.27,76.99 \ /*
*/ 107.66,78.68 \ /*
*/ 110.04,80.37 \ /*
*/ 112.43,82.05 \ /*
*/ 114.82,83.74 \ /*
*/ 117.21,85.42 \ /*
*/ 119.59,87.11 \ /*
*/ 121.98,88.8 \ /*
*/ 124.37,90.48 \ /*
*/ 126.75,92.17 \ /*
*/ 129.14,93.85 \ /*
*/ 131.53,95.54 \ /*
*/ 133.92,97.23 \ /*
*/ 136.3,98.91 \ /*
*/ 138.69,100.6 \ /*
*/ 141.08,102.29 \ /*
*/ 143.47,103.97 \ /*
*/ 145.85,105.66 \ /*
*/ 148.24,107.34 \ /*
*/ 150.63,109.03 \ /*
*/ 153.01,110.72 \ /*
*/ 155.4,112.4 \ /*
*/ 157.79,114.09 \ /*
*/ 160.18,115.77 \ /*
*/ 162.56,117.46 \ /*
*/ 164.95,119.15 \ /*
*/ 167.34,120.83 \ /*
*/ 169.72,122.52 \ /*
*/ 172.11,124.2 \ /*
*/ 174.5,125.89 \ /*
*/ 176.89,127.58 \ /*
*/ 179.27,129.26 \ /*
*/ 181.66,130.95 \ /*
*/ 184.05,132.63 \ /*
*/ 186.44,134.32 \ /*
*/ 188.82,136.01 \ /*
*/ 191.21,137.69 \ /*
*/ 193.6,139.38 \ /*
*/ 195.98,141.07 \ /*
*/ 198.37,142.75 \ /*
*/ 200.76,144.44 \ /*
*/ 203.15,146.12 \ /*
*/ 205.53,147.81 \ /*
*/ 207.92,149.5 \ /*
*/ 210.31,151.18 \ /*
*/ 212.69,152.87 \ /*
*/ 215.08,154.55 \ /*
*/ 217.47,156.24 \ /*
*/ 219.86,157.93 \ /*
*/ 222.24,159.61 \ /*
*/ 224.63,161.3 \ /*
*/ 227.02,162.98 \ /*
*/ 229.41,164.67 \ /*
*/ 231.79,166.36 \ /*
*/ 234.18,168.04 \ /*
*/ 236.57,169.73 \ /*
*/ 238.95,171.41 \ /*
*/ 241.34,173.1 \ /*
*/ 243.73,174.79 \ /*
*/ 246.12,176.47 \ /*
*/ 248.5,178.16 \ /*
*/ 250.89,179.84 \ /*
*/ 253.28,181.53)
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="ivsize15" {
matrix input `temp' = ( /*
*/ 8.96 , . \ /*
*/ 11.59 , 4.58 \ /*
*/ 12.83 , 8.18 \ /*
*/ 13.96 , 9.93 \ /*
*/ 15.09 , 11.22 \ /*
*/ 16.23 , 12.33 \ /*
*/ 17.38 , 13.34 \ /*
*/ 18.54 , 14.31 \ /*
*/ 19.71 , 15.24 \ /*
*/ 20.88 , 16.16 \ /*
*/ 22.06 , 17.06 \ /*
*/ 23.24 , 17.95 \ /*
*/ 24.42 , 18.84 \ /*
*/ 25.61 , 19.72 \ /*
*/ 26.80 , 20.60 \ /*
*/ 27.99 , 21.48 \ /*
*/ 29.19 , 22.35 \ /*
*/ 30.38 , 23.22 \ /*
*/ 31.58 , 24.09 \ /*
*/ 32.77 , 24.96 \ /*
*/ 33.97 , 25.82 \ /*
*/ 35.17 , 26.69 \ /*
*/ 36.37 , 27.56 \ /*
*/ 37.57 , 28.42 \ /*
*/ 38.77 , 29.29 \ /*
*/ 39.97 , 30.15 \ /*
*/ 41.17 , 31.02 \ /*
*/ 42.37 , 31.88 \ /*
*/ 43.57 , 32.74 \ /*
*/ 44.78 , 33.61 \ /*
*/ 45.98 , 34.47 \ /*
*/ 47.18 , 35.33 \ /*
*/ 48.38 , 36.19 \ /*
*/ 49.59 , 37.06 \ /*
*/ 50.79 , 37.92 \ /*
*/ 51.99 , 38.78 \ /*
*/ 53.19 , 39.64 \ /*
*/ 54.40 , 40.50 \ /*
*/ 55.60 , 41.37 \ /*
*/ 56.80 , 42.23 \ /*
*/ 58.01 , 43.09 \ /*
*/ 59.21 , 43.95 \ /*
*/ 60.41 , 44.81 \ /*
*/ 61.61 , 45.68 \ /*
*/ 62.82 , 46.54 \ /*
*/ 64.02 , 47.40 \ /*
*/ 65.22 , 48.26 \ /*
*/ 66.42 , 49.12 \ /*
*/ 67.63 , 49.99 \ /*
*/ 68.83 , 50.85 \ /*
*/ 70.03 , 51.71 \ /*
*/ 71.24 , 52.57 \ /*
*/ 72.44 , 53.43 \ /*
*/ 73.64 , 54.30 \ /*
*/ 74.84 , 55.16 \ /*
*/ 76.05 , 56.02 \ /*
*/ 77.25 , 56.88 \ /*
*/ 78.45 , 57.74 \ /*
*/ 79.66 , 58.61 \ /*
*/ 80.86 , 59.47 \ /*
*/ 82.06 , 60.33 \ /*
*/ 83.26 , 61.19 \ /*
*/ 84.47 , 62.05 \ /*
*/ 85.67 , 62.92 \ /*
*/ 86.87 , 63.78 \ /*
*/ 88.07 , 64.64 \ /*
*/ 89.28 , 65.50 \ /*
*/ 90.48 , 66.36 \ /*
*/ 91.68 , 67.22 \ /*
*/ 92.89 , 68.09 \ /*
*/ 94.09 , 68.95 \ /*
*/ 95.29 , 69.81 \ /*
*/ 96.49 , 70.67 \ /*
*/ 97.70 , 71.53 \ /*
*/ 98.90 , 72.40 \ /*
*/ 100.10 , 73.26 \ /*
*/ 101.30 , 74.12 \ /*
*/ 102.51 , 74.98 \ /*
*/ 103.71 , 75.84 \ /*
*/ 104.91 , 76.71 \ /*
*/ 106.12 , 77.57 \ /*
*/ 107.32 , 78.43 \ /*
*/ 108.52 , 79.29 \ /*
*/ 109.72 , 80.15 \ /*
*/ 110.93 , 81.02 \ /*
*/ 112.13 , 81.88 \ /*
*/ 113.33 , 82.74 \ /*
*/ 114.53 , 83.60 \ /*
*/ 115.74 , 84.46 \ /*
*/ 116.94 , 85.33 \ /*
*/ 118.14 , 86.19 \ /*
*/ 119.35 , 87.05 \ /*
*/ 120.55 , 87.91 \ /*
*/ 121.75 , 88.77 \ /*
*/ 122.95 , 89.64 \ /*
*/ 124.16 , 90.50 \ /*
*/ 125.36 , 91.36 \ /*
*/ 126.56 , 92.22 \ /*
*/ 127.76 , 93.08 \ /*
*/ 128.97 , 93.95 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="ivsize20" {
matrix input `temp' = ( /*
*/ 6.66 , . \ /*
*/ 8.75 , 3.95 \ /*
*/ 9.54 , 6.40 \ /*
*/ 10.26 , 7.54 \ /*
*/ 10.98 , 8.38 \ /*
*/ 11.72 , 9.10 \ /*
*/ 12.48 , 9.77 \ /*
*/ 13.24 , 10.41 \ /*
*/ 14.01 , 11.03 \ /*
*/ 14.78 , 11.65 \ /*
*/ 15.56 , 12.25 \ /*
*/ 16.35 , 12.86 \ /*
*/ 17.14 , 13.45 \ /*
*/ 17.93 , 14.05 \ /*
*/ 18.72 , 14.65 \ /*
*/ 19.51 , 15.24 \ /*
*/ 20.31 , 15.83 \ /*
*/ 21.10 , 16.42 \ /*
*/ 21.90 , 17.02 \ /*
*/ 22.70 , 17.61 \ /*
*/ 23.50 , 18.20 \ /*
*/ 24.30 , 18.79 \ /*
*/ 25.10 , 19.38 \ /*
*/ 25.90 , 19.97 \ /*
*/ 26.71 , 20.56 \ /*
*/ 27.51 , 21.15 \ /*
*/ 28.31 , 21.74 \ /*
*/ 29.12 , 22.33 \ /*
*/ 29.92 , 22.92 \ /*
*/ 30.72 , 23.51 \ /*
*/ 31.53 , 24.10 \ /*
*/ 32.33 , 24.69 \ /*
*/ 33.14 , 25.28 \ /*
*/ 33.94 , 25.87 \ /*
*/ 34.75 , 26.46 \ /*
*/ 35.55 , 27.05 \ /*
*/ 36.36 , 27.64 \ /*
*/ 37.17 , 28.23 \ /*
*/ 37.97 , 28.82 \ /*
*/ 38.78 , 29.41 \ /*
*/ 39.58 , 30.00 \ /*
*/ 40.39 , 30.59 \ /*
*/ 41.20 , 31.18 \ /*
*/ 42.00 , 31.77 \ /*
*/ 42.81 , 32.36 \ /*
*/ 43.62 , 32.95 \ /*
*/ 44.42 , 33.54 \ /*
*/ 45.23 , 34.13 \ /*
*/ 46.03 , 34.72 \ /*
*/ 46.84 , 35.31 \ /*
*/ 47.65 , 35.90 \ /*
*/ 48.45 , 36.49 \ /*
*/ 49.26 , 37.08 \ /*
*/ 50.06 , 37.67 \ /*
*/ 50.87 , 38.26 \ /*
*/ 51.68 , 38.85 \ /*
*/ 52.48 , 39.44 \ /*
*/ 53.29 , 40.02 \ /*
*/ 54.09 , 40.61 \ /*
*/ 54.90 , 41.20 \ /*
*/ 55.71 , 41.79 \ /*
*/ 56.51 , 42.38 \ /*
*/ 57.32 , 42.97 \ /*
*/ 58.13 , 43.56 \ /*
*/ 58.93 , 44.15 \ /*
*/ 59.74 , 44.74 \ /*
*/ 60.54 , 45.33 \ /*
*/ 61.35 , 45.92 \ /*
*/ 62.16 , 46.51 \ /*
*/ 62.96 , 47.10 \ /*
*/ 63.77 , 47.69 \ /*
*/ 64.57 , 48.28 \ /*
*/ 65.38 , 48.87 \ /*
*/ 66.19 , 49.46 \ /*
*/ 66.99 , 50.05 \ /*
*/ 67.80 , 50.64 \ /*
*/ 68.60 , 51.23 \ /*
*/ 69.41 , 51.82 \ /*
*/ 70.22 , 52.41 \ /*
*/ 71.02 , 53.00 \ /*
*/ 71.83 , 53.59 \ /*
*/ 72.64 , 54.18 \ /*
*/ 73.44 , 54.77 \ /*
*/ 74.25 , 55.36 \ /*
*/ 75.05 , 55.95 \ /*
*/ 75.86 , 56.54 \ /*
*/ 76.67 , 57.13 \ /*
*/ 77.47 , 57.72 \ /*
*/ 78.28 , 58.31 \ /*
*/ 79.08 , 58.90 \ /*
*/ 79.89 , 59.49 \ /*
*/ 80.70 , 60.08 \ /*
*/ 81.50 , 60.67 \ /*
*/ 82.31 , 61.26 \ /*
*/ 83.12 , 61.85 \ /*
*/ 83.92 , 62.44 \ /*
*/ 84.73 , 63.03 \ /*
*/ 85.53 , 63.62 \ /*
*/ 86.34 , 64.21 \ /*
*/ 87.15 , 64.80 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="ivsize25" {
matrix input `temp' = ( /*
*/ 5.53 , . \ /*
*/ 7.25 , 3.63 \ /*
*/ 7.80 , 5.45 \ /*
*/ 8.31 , 6.28 \ /*
*/ 8.84 , 6.89 \ /*
*/ 9.38 , 7.42 \ /*
*/ 9.93 , 7.91 \ /*
*/ 10.50 , 8.39 \ /*
*/ 11.07 , 8.85 \ /*
*/ 11.65 , 9.31 \ /*
*/ 12.23 , 9.77 \ /*
*/ 12.82 , 10.22 \ /*
*/ 13.41 , 10.68 \ /*
*/ 14.00 , 11.13 \ /*
*/ 14.60 , 11.58 \ /*
*/ 15.19 , 12.03 \ /*
*/ 15.79 , 12.49 \ /*
*/ 16.39 , 12.94 \ /*
*/ 16.99 , 13.39 \ /*
*/ 17.60 , 13.84 \ /*
*/ 18.20 , 14.29 \ /*
*/ 18.80 , 14.74 \ /*
*/ 19.41 , 15.19 \ /*
*/ 20.01 , 15.64 \ /*
*/ 20.61 , 16.10 \ /*
*/ 21.22 , 16.55 \ /*
*/ 21.83 , 17.00 \ /*
*/ 22.43 , 17.45 \ /*
*/ 23.04 , 17.90 \ /*
*/ 23.65 , 18.35 \ /*
*/ 24.25 , 18.81 \ /*
*/ 24.86 , 19.26 \ /*
*/ 25.47 , 19.71 \ /*
*/ 26.08 , 20.16 \ /*
*/ 26.68 , 20.61 \ /*
*/ 27.29 , 21.06 \ /*
*/ 27.90 , 21.52 \ /*
*/ 28.51 , 21.97 \ /*
*/ 29.12 , 22.42 \ /*
*/ 29.73 , 22.87 \ /*
*/ 30.33 , 23.32 \ /*
*/ 30.94 , 23.78 \ /*
*/ 31.55 , 24.23 \ /*
*/ 32.16 , 24.68 \ /*
*/ 32.77 , 25.13 \ /*
*/ 33.38 , 25.58 \ /*
*/ 33.99 , 26.04 \ /*
*/ 34.60 , 26.49 \ /*
*/ 35.21 , 26.94 \ /*
*/ 35.82 , 27.39 \ /*
*/ 36.43 , 27.85 \ /*
*/ 37.04 , 28.30 \ /*
*/ 37.65 , 28.75 \ /*
*/ 38.25 , 29.20 \ /*
*/ 38.86 , 29.66 \ /*
*/ 39.47 , 30.11 \ /*
*/ 40.08 , 30.56 \ /*
*/ 40.69 , 31.01 \ /*
*/ 41.30 , 31.47 \ /*
*/ 41.91 , 31.92 \ /*
*/ 42.52 , 32.37 \ /*
*/ 43.13 , 32.82 \ /*
*/ 43.74 , 33.27 \ /*
*/ 44.35 , 33.73 \ /*
*/ 44.96 , 34.18 \ /*
*/ 45.57 , 34.63 \ /*
*/ 46.18 , 35.08 \ /*
*/ 46.78 , 35.54 \ /*
*/ 47.39 , 35.99 \ /*
*/ 48.00 , 36.44 \ /*
*/ 48.61 , 36.89 \ /*
*/ 49.22 , 37.35 \ /*
*/ 49.83 , 37.80 \ /*
*/ 50.44 , 38.25 \ /*
*/ 51.05 , 38.70 \ /*
*/ 51.66 , 39.16 \ /*
*/ 52.27 , 39.61 \ /*
*/ 52.88 , 40.06 \ /*
*/ 53.49 , 40.51 \ /*
*/ 54.10 , 40.96 \ /*
*/ 54.71 , 41.42 \ /*
*/ 55.32 , 41.87 \ /*
*/ 55.92 , 42.32 \ /*
*/ 56.53 , 42.77 \ /*
*/ 57.14 , 43.23 \ /*
*/ 57.75 , 43.68 \ /*
*/ 58.36 , 44.13 \ /*
*/ 58.97 , 44.58 \ /*
*/ 59.58 , 45.04 \ /*
*/ 60.19 , 45.49 \ /*
*/ 60.80 , 45.94 \ /*
*/ 61.41 , 46.39 \ /*
*/ 62.02 , 46.85 \ /*
*/ 62.63 , 47.30 \ /*
*/ 63.24 , 47.75 \ /*
*/ 63.85 , 48.20 \ /*
*/ 64.45 , 48.65 \ /*
*/ 65.06 , 49.11 \ /*
*/ 65.67 , 49.56 \ /*
*/ 66.28 , 50.01 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="fullrel5" {
matrix input `temp' = ( /*
*/ 24.09 , . \ /*
*/ 13.46 , 15.50 \ /*
*/ 9.61 , 10.83 \ /*
*/ 7.63 , 8.53 \ /*
*/ 6.42 , 7.16 \ /*
*/ 5.61 , 6.24 \ /*
*/ 5.02 , 5.59 \ /*
*/ 4.58 , 5.10 \ /*
*/ 4.23 , 4.71 \ /*
*/ 3.96 , 4.41 \ /*
*/ 3.73 , 4.15 \ /*
*/ 3.54 , 3.94 \ /*
*/ 3.38 , 3.76 \ /*
*/ 3.24 , 3.60 \ /*
*/ 3.12 , 3.47 \ /*
*/ 3.01 , 3.35 \ /*
*/ 2.92 , 3.24 \ /*
*/ 2.84 , 3.15 \ /*
*/ 2.76 , 3.06 \ /*
*/ 2.69 , 2.98 \ /*
*/ 2.63 , 2.91 \ /*
*/ 2.58 , 2.85 \ /*
*/ 2.52 , 2.79 \ /*
*/ 2.48 , 2.73 \ /*
*/ 2.43 , 2.68 \ /*
*/ 2.39 , 2.63 \ /*
*/ 2.36 , 2.59 \ /*
*/ 2.32 , 2.55 \ /*
*/ 2.29 , 2.51 \ /*
*/ 2.26 , 2.47 \ /*
*/ 2.23 , 2.44 \ /*
*/ 2.20 , 2.41 \ /*
*/ 2.18 , 2.37 \ /*
*/ 2.16 , 2.35 \ /*
*/ 2.13 , 2.32 \ /*
*/ 2.11 , 2.29 \ /*
*/ 2.09 , 2.27 \ /*
*/ 2.07 , 2.24 \ /*
*/ 2.05 , 2.22 \ /*
*/ 2.04 , 2.20 \ /*
*/ 2.02 , 2.18 \ /*
*/ 2.00 , 2.16 \ /*
*/ 1.99 , 2.14 \ /*
*/ 1.97 , 2.12 \ /*
*/ 1.96 , 2.10 \ /*
*/ 1.94 , 2.09 \ /*
*/ 1.93 , 2.07 \ /*
*/ 1.92 , 2.05 \ /*
*/ 1.91 , 2.04 \ /*
*/ 1.89 , 2.02 \ /*
*/ 1.88 , 2.01 \ /*
*/ 1.87 , 2.00 \ /*
*/ 1.86 , 1.98 \ /*
*/ 1.85 , 1.97 \ /*
*/ 1.84 , 1.96 \ /*
*/ 1.83 , 1.95 \ /*
*/ 1.82 , 1.94 \ /*
*/ 1.81 , 1.92 \ /*
*/ 1.80 , 1.91 \ /*
*/ 1.79 , 1.90 \ /*
*/ 1.79 , 1.89 \ /*
*/ 1.78 , 1.88 \ /*
*/ 1.77 , 1.87 \ /*
*/ 1.76 , 1.87 \ /*
*/ 1.75 , 1.86 \ /*
*/ 1.75 , 1.85 \ /*
*/ 1.74 , 1.84 \ /*
*/ 1.73 , 1.83 \ /*
*/ 1.72 , 1.83 \ /*
*/ 1.72 , 1.82 \ /*
*/ 1.71 , 1.81 \ /*
*/ 1.70 , 1.80 \ /*
*/ 1.70 , 1.80 \ /*
*/ 1.69 , 1.79 \ /*
*/ 1.68 , 1.79 \ /*
*/ 1.68 , 1.78 \ /*
*/ 1.67 , 1.77 \ /*
*/ 1.67 , 1.77 \ /*
*/ 1.66 , 1.76 \ /*
*/ 1.65 , 1.76 \ /*
*/ 1.65 , 1.75 \ /*
*/ 1.64 , 1.75 \ /*
*/ 1.64 , 1.74 \ /*
*/ 1.63 , 1.74 \ /*
*/ 1.63 , 1.73 \ /*
*/ 1.62 , 1.73 \ /*
*/ 1.61 , 1.73 \ /*
*/ 1.61 , 1.72 \ /*
*/ 1.60 , 1.72 \ /*
*/ 1.60 , 1.71 \ /*
*/ 1.59 , 1.71 \ /*
*/ 1.59 , 1.71 \ /*
*/ 1.58 , 1.71 \ /*
*/ 1.58 , 1.70 \ /*
*/ 1.57 , 1.70 \ /*
*/ 1.57 , 1.70 \ /*
*/ 1.56 , 1.69 \ /*
*/ 1.56 , 1.69 \ /*
*/ 1.55 , 1.69 \ /*
*/ 1.55 , 1.69 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="fullrel10" {
matrix input `temp' = ( /*
*/ 19.36 , . \ /*
*/ 10.89 , 12.55 \ /*
*/ 7.90 , 8.96 \ /*
*/ 6.37 , 7.15 \ /*
*/ 5.44 , 6.07 \ /*
*/ 4.81 , 5.34 \ /*
*/ 4.35 , 4.82 \ /*
*/ 4.01 , 4.43 \ /*
*/ 3.74 , 4.12 \ /*
*/ 3.52 , 3.87 \ /*
*/ 3.34 , 3.67 \ /*
*/ 3.19 , 3.49 \ /*
*/ 3.06 , 3.35 \ /*
*/ 2.95 , 3.22 \ /*
*/ 2.85 , 3.11 \ /*
*/ 2.76 , 3.01 \ /*
*/ 2.69 , 2.92 \ /*
*/ 2.62 , 2.84 \ /*
*/ 2.56 , 2.77 \ /*
*/ 2.50 , 2.71 \ /*
*/ 2.45 , 2.65 \ /*
*/ 2.40 , 2.60 \ /*
*/ 2.36 , 2.55 \ /*
*/ 2.32 , 2.50 \ /*
*/ 2.28 , 2.46 \ /*
*/ 2.24 , 2.42 \ /*
*/ 2.21 , 2.38 \ /*
*/ 2.18 , 2.35 \ /*
*/ 2.15 , 2.31 \ /*
*/ 2.12 , 2.28 \ /*
*/ 2.10 , 2.25 \ /*
*/ 2.07 , 2.23 \ /*
*/ 2.05 , 2.20 \ /*
*/ 2.03 , 2.17 \ /*
*/ 2.01 , 2.15 \ /*
*/ 1.99 , 2.13 \ /*
*/ 1.97 , 2.11 \ /*
*/ 1.95 , 2.09 \ /*
*/ 1.93 , 2.07 \ /*
*/ 1.92 , 2.05 \ /*
*/ 1.90 , 2.03 \ /*
*/ 1.88 , 2.01 \ /*
*/ 1.87 , 2.00 \ /*
*/ 1.86 , 1.98 \ /*
*/ 1.84 , 1.96 \ /*
*/ 1.83 , 1.95 \ /*
*/ 1.82 , 1.93 \ /*
*/ 1.81 , 1.92 \ /*
*/ 1.79 , 1.91 \ /*
*/ 1.78 , 1.89 \ /*
*/ 1.77 , 1.88 \ /*
*/ 1.76 , 1.87 \ /*
*/ 1.75 , 1.86 \ /*
*/ 1.74 , 1.85 \ /*
*/ 1.73 , 1.84 \ /*
*/ 1.72 , 1.83 \ /*
*/ 1.71 , 1.82 \ /*
*/ 1.70 , 1.81 \ /*
*/ 1.70 , 1.80 \ /*
*/ 1.69 , 1.79 \ /*
*/ 1.68 , 1.78 \ /*
*/ 1.67 , 1.77 \ /*
*/ 1.67 , 1.76 \ /*
*/ 1.66 , 1.75 \ /*
*/ 1.65 , 1.75 \ /*
*/ 1.64 , 1.74 \ /*
*/ 1.64 , 1.73 \ /*
*/ 1.63 , 1.72 \ /*
*/ 1.63 , 1.72 \ /*
*/ 1.62 , 1.71 \ /*
*/ 1.61 , 1.70 \ /*
*/ 1.61 , 1.70 \ /*
*/ 1.60 , 1.69 \ /*
*/ 1.60 , 1.68 \ /*
*/ 1.59 , 1.68 \ /*
*/ 1.59 , 1.67 \ /*
*/ 1.58 , 1.67 \ /*
*/ 1.58 , 1.66 \ /*
*/ 1.57 , 1.66 \ /*
*/ 1.57 , 1.65 \ /*
*/ 1.56 , 1.65 \ /*
*/ 1.56 , 1.64 \ /*
*/ 1.56 , 1.64 \ /*
*/ 1.55 , 1.63 \ /*
*/ 1.55 , 1.63 \ /*
*/ 1.54 , 1.62 \ /*
*/ 1.54 , 1.62 \ /*
*/ 1.54 , 1.62 \ /*
*/ 1.53 , 1.61 \ /*
*/ 1.53 , 1.61 \ /*
*/ 1.53 , 1.61 \ /*
*/ 1.52 , 1.60 \ /*
*/ 1.52 , 1.60 \ /*
*/ 1.52 , 1.60 \ /*
*/ 1.52 , 1.59 \ /*
*/ 1.51 , 1.59 \ /*
*/ 1.51 , 1.59 \ /*
*/ 1.51 , 1.59 \ /*
*/ 1.51 , 1.58 \ /*
*/ 1.50 , 1.58 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="fullrel20" {
matrix input `temp' = ( /*
*/ 15.64 , . \ /*
*/ 9.00 , 9.72 \ /*
*/ 6.61 , 7.18 \ /*
*/ 5.38 , 5.85 \ /*
*/ 4.62 , 5.04 \ /*
*/ 4.11 , 4.48 \ /*
*/ 3.75 , 4.08 \ /*
*/ 3.47 , 3.77 \ /*
*/ 3.25 , 3.53 \ /*
*/ 3.07 , 3.33 \ /*
*/ 2.92 , 3.17 \ /*
*/ 2.80 , 3.04 \ /*
*/ 2.70 , 2.92 \ /*
*/ 2.61 , 2.82 \ /*
*/ 2.53 , 2.73 \ /*
*/ 2.46 , 2.65 \ /*
*/ 2.39 , 2.58 \ /*
*/ 2.34 , 2.52 \ /*
*/ 2.29 , 2.46 \ /*
*/ 2.24 , 2.41 \ /*
*/ 2.20 , 2.36 \ /*
*/ 2.16 , 2.32 \ /*
*/ 2.13 , 2.28 \ /*
*/ 2.10 , 2.24 \ /*
*/ 2.06 , 2.21 \ /*
*/ 2.04 , 2.18 \ /*
*/ 2.01 , 2.15 \ /*
*/ 1.99 , 2.12 \ /*
*/ 1.96 , 2.09 \ /*
*/ 1.94 , 2.07 \ /*
*/ 1.92 , 2.04 \ /*
*/ 1.90 , 2.02 \ /*
*/ 1.88 , 2.00 \ /*
*/ 1.87 , 1.98 \ /*
*/ 1.85 , 1.96 \ /*
*/ 1.83 , 1.94 \ /*
*/ 1.82 , 1.93 \ /*
*/ 1.80 , 1.91 \ /*
*/ 1.79 , 1.89 \ /*
*/ 1.78 , 1.88 \ /*
*/ 1.76 , 1.86 \ /*
*/ 1.75 , 1.85 \ /*
*/ 1.74 , 1.84 \ /*
*/ 1.73 , 1.82 \ /*
*/ 1.72 , 1.81 \ /*
*/ 1.71 , 1.80 \ /*
*/ 1.70 , 1.79 \ /*
*/ 1.69 , 1.78 \ /*
*/ 1.68 , 1.77 \ /*
*/ 1.67 , 1.76 \ /*
*/ 1.66 , 1.75 \ /*
*/ 1.65 , 1.74 \ /*
*/ 1.65 , 1.73 \ /*
*/ 1.64 , 1.72 \ /*
*/ 1.63 , 1.71 \ /*
*/ 1.62 , 1.70 \ /*
*/ 1.62 , 1.69 \ /*
*/ 1.61 , 1.68 \ /*
*/ 1.60 , 1.68 \ /*
*/ 1.60 , 1.67 \ /*
*/ 1.59 , 1.66 \ /*
*/ 1.58 , 1.65 \ /*
*/ 1.58 , 1.65 \ /*
*/ 1.57 , 1.64 \ /*
*/ 1.57 , 1.63 \ /*
*/ 1.56 , 1.63 \ /*
*/ 1.56 , 1.62 \ /*
*/ 1.55 , 1.62 \ /*
*/ 1.55 , 1.61 \ /*
*/ 1.54 , 1.60 \ /*
*/ 1.54 , 1.60 \ /*
*/ 1.53 , 1.59 \ /*
*/ 1.53 , 1.59 \ /*
*/ 1.52 , 1.58 \ /*
*/ 1.52 , 1.58 \ /*
*/ 1.51 , 1.57 \ /*
*/ 1.51 , 1.57 \ /*
*/ 1.51 , 1.56 \ /*
*/ 1.50 , 1.56 \ /*
*/ 1.50 , 1.56 \ /*
*/ 1.49 , 1.55 \ /*
*/ 1.49 , 1.55 \ /*
*/ 1.49 , 1.54 \ /*
*/ 1.48 , 1.54 \ /*
*/ 1.48 , 1.54 \ /*
*/ 1.48 , 1.53 \ /*
*/ 1.47 , 1.53 \ /*
*/ 1.47 , 1.53 \ /*
*/ 1.47 , 1.52 \ /*
*/ 1.46 , 1.52 \ /*
*/ 1.46 , 1.52 \ /*
*/ 1.46 , 1.51 \ /*
*/ 1.46 , 1.51 \ /*
*/ 1.45 , 1.51 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.44 , 1.50 \ /*
*/ 1.44 , 1.49 \ /*
*/ 1.44 , 1.49 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="fullrel30" {
matrix input `temp' = ( /*
*/ 12.71 , . \ /*
*/ 7.49 , 8.03 \ /*
*/ 5.60 , 6.15 \ /*
*/ 4.63 , 5.10 \ /*
*/ 4.03 , 4.44 \ /*
*/ 3.63 , 3.98 \ /*
*/ 3.33 , 3.65 \ /*
*/ 3.11 , 3.39 \ /*
*/ 2.93 , 3.19 \ /*
*/ 2.79 , 3.02 \ /*
*/ 2.67 , 2.88 \ /*
*/ 2.57 , 2.77 \ /*
*/ 2.48 , 2.67 \ /*
*/ 2.41 , 2.58 \ /*
*/ 2.34 , 2.51 \ /*
*/ 2.28 , 2.44 \ /*
*/ 2.23 , 2.38 \ /*
*/ 2.18 , 2.33 \ /*
*/ 2.14 , 2.28 \ /*
*/ 2.10 , 2.23 \ /*
*/ 2.07 , 2.19 \ /*
*/ 2.04 , 2.16 \ /*
*/ 2.01 , 2.12 \ /*
*/ 1.98 , 2.09 \ /*
*/ 1.95 , 2.06 \ /*
*/ 1.93 , 2.03 \ /*
*/ 1.90 , 2.01 \ /*
*/ 1.88 , 1.98 \ /*
*/ 1.86 , 1.96 \ /*
*/ 1.84 , 1.94 \ /*
*/ 1.83 , 1.92 \ /*
*/ 1.81 , 1.90 \ /*
*/ 1.79 , 1.88 \ /*
*/ 1.78 , 1.87 \ /*
*/ 1.76 , 1.85 \ /*
*/ 1.75 , 1.83 \ /*
*/ 1.74 , 1.82 \ /*
*/ 1.72 , 1.80 \ /*
*/ 1.71 , 1.79 \ /*
*/ 1.70 , 1.78 \ /*
*/ 1.69 , 1.77 \ /*
*/ 1.68 , 1.75 \ /*
*/ 1.67 , 1.74 \ /*
*/ 1.66 , 1.73 \ /*
*/ 1.65 , 1.72 \ /*
*/ 1.64 , 1.71 \ /*
*/ 1.63 , 1.70 \ /*
*/ 1.62 , 1.69 \ /*
*/ 1.61 , 1.68 \ /*
*/ 1.60 , 1.67 \ /*
*/ 1.60 , 1.66 \ /*
*/ 1.59 , 1.66 \ /*
*/ 1.58 , 1.65 \ /*
*/ 1.57 , 1.64 \ /*
*/ 1.57 , 1.63 \ /*
*/ 1.56 , 1.63 \ /*
*/ 1.55 , 1.62 \ /*
*/ 1.55 , 1.61 \ /*
*/ 1.54 , 1.61 \ /*
*/ 1.54 , 1.60 \ /*
*/ 1.53 , 1.59 \ /*
*/ 1.53 , 1.59 \ /*
*/ 1.52 , 1.58 \ /*
*/ 1.51 , 1.57 \ /*
*/ 1.51 , 1.57 \ /*
*/ 1.50 , 1.56 \ /*
*/ 1.50 , 1.56 \ /*
*/ 1.50 , 1.55 \ /*
*/ 1.49 , 1.55 \ /*
*/ 1.49 , 1.54 \ /*
*/ 1.48 , 1.54 \ /*
*/ 1.48 , 1.53 \ /*
*/ 1.47 , 1.53 \ /*
*/ 1.47 , 1.52 \ /*
*/ 1.47 , 1.52 \ /*
*/ 1.46 , 1.52 \ /*
*/ 1.46 , 1.51 \ /*
*/ 1.46 , 1.51 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.44 , 1.49 \ /*
*/ 1.44 , 1.49 \ /*
*/ 1.44 , 1.48 \ /*
*/ 1.43 , 1.48 \ /*
*/ 1.43 , 1.48 \ /*
*/ 1.43 , 1.47 \ /*
*/ 1.43 , 1.47 \ /*
*/ 1.42 , 1.47 \ /*
*/ 1.42 , 1.47 \ /*
*/ 1.42 , 1.46 \ /*
*/ 1.42 , 1.46 \ /*
*/ 1.41 , 1.46 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.40 , 1.44 \ /*
*/ 1.40 , 1.44 \ /*
*/ 1.40 , 1.44 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="fullmax5" {
matrix input `temp' = ( /*
*/ 23.81 , . \ /*
*/ 12.38 , 14.19 \ /*
*/ 8.66 , 10.00 \ /*
*/ 6.81 , 7.88 \ /*
*/ 5.71 , 6.60 \ /*
*/ 4.98 , 5.74 \ /*
*/ 4.45 , 5.13 \ /*
*/ 4.06 , 4.66 \ /*
*/ 3.76 , 4.30 \ /*
*/ 3.51 , 4.01 \ /*
*/ 3.31 , 3.77 \ /*
*/ 3.15 , 3.57 \ /*
*/ 3.00 , 3.41 \ /*
*/ 2.88 , 3.26 \ /*
*/ 2.78 , 3.13 \ /*
*/ 2.69 , 3.02 \ /*
*/ 2.61 , 2.92 \ /*
*/ 2.53 , 2.84 \ /*
*/ 2.47 , 2.76 \ /*
*/ 2.41 , 2.69 \ /*
*/ 2.36 , 2.62 \ /*
*/ 2.31 , 2.56 \ /*
*/ 2.27 , 2.51 \ /*
*/ 2.23 , 2.46 \ /*
*/ 2.19 , 2.42 \ /*
*/ 2.15 , 2.37 \ /*
*/ 2.12 , 2.33 \ /*
*/ 2.09 , 2.30 \ /*
*/ 2.07 , 2.26 \ /*
*/ 2.04 , 2.23 \ /*
*/ 2.02 , 2.20 \ /*
*/ 1.99 , 2.17 \ /*
*/ 1.97 , 2.14 \ /*
*/ 1.95 , 2.12 \ /*
*/ 1.93 , 2.10 \ /*
*/ 1.91 , 2.07 \ /*
*/ 1.90 , 2.05 \ /*
*/ 1.88 , 2.03 \ /*
*/ 1.87 , 2.01 \ /*
*/ 1.85 , 1.99 \ /*
*/ 1.84 , 1.98 \ /*
*/ 1.82 , 1.96 \ /*
*/ 1.81 , 1.94 \ /*
*/ 1.80 , 1.93 \ /*
*/ 1.79 , 1.91 \ /*
*/ 1.78 , 1.90 \ /*
*/ 1.76 , 1.88 \ /*
*/ 1.75 , 1.87 \ /*
*/ 1.74 , 1.86 \ /*
*/ 1.73 , 1.85 \ /*
*/ 1.73 , 1.83 \ /*
*/ 1.72 , 1.82 \ /*
*/ 1.71 , 1.81 \ /*
*/ 1.70 , 1.80 \ /*
*/ 1.69 , 1.79 \ /*
*/ 1.68 , 1.78 \ /*
*/ 1.68 , 1.77 \ /*
*/ 1.67 , 1.76 \ /*
*/ 1.66 , 1.75 \ /*
*/ 1.65 , 1.74 \ /*
*/ 1.65 , 1.74 \ /*
*/ 1.64 , 1.73 \ /*
*/ 1.63 , 1.72 \ /*
*/ 1.63 , 1.71 \ /*
*/ 1.62 , 1.70 \ /*
*/ 1.62 , 1.70 \ /*
*/ 1.61 , 1.69 \ /*
*/ 1.60 , 1.68 \ /*
*/ 1.60 , 1.68 \ /*
*/ 1.59 , 1.67 \ /*
*/ 1.59 , 1.66 \ /*
*/ 1.58 , 1.66 \ /*
*/ 1.58 , 1.65 \ /*
*/ 1.57 , 1.64 \ /*
*/ 1.57 , 1.64 \ /*
*/ 1.56 , 1.63 \ /*
*/ 1.56 , 1.63 \ /*
*/ 1.55 , 1.62 \ /*
*/ 1.55 , 1.62 \ /*
*/ 1.54 , 1.61 \ /*
*/ 1.54 , 1.61 \ /*
*/ 1.53 , 1.60 \ /*
*/ 1.53 , 1.60 \ /*
*/ 1.53 , 1.59 \ /*
*/ 1.52 , 1.59 \ /*
*/ 1.52 , 1.58 \ /*
*/ 1.51 , 1.58 \ /*
*/ 1.51 , 1.57 \ /*
*/ 1.50 , 1.57 \ /*
*/ 1.50 , 1.57 \ /*
*/ 1.50 , 1.56 \ /*
*/ 1.49 , 1.56 \ /*
*/ 1.49 , 1.55 \ /*
*/ 1.49 , 1.55 \ /*
*/ 1.48 , 1.55 \ /*
*/ 1.48 , 1.54 \ /*
*/ 1.47 , 1.54 \ /*
*/ 1.47 , 1.54 \ /*
*/ 1.47 , 1.53 \ /*
*/ 1.46 , 1.53 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="fullmax10" {
matrix input `temp' = ( /*
*/ 19.40 , . \ /*
*/ 10.14 , 11.92 \ /*
*/ 7.18 , 8.39 \ /*
*/ 5.72 , 6.64 \ /*
*/ 4.85 , 5.60 \ /*
*/ 4.27 , 4.90 \ /*
*/ 3.86 , 4.40 \ /*
*/ 3.55 , 4.03 \ /*
*/ 3.31 , 3.73 \ /*
*/ 3.12 , 3.50 \ /*
*/ 2.96 , 3.31 \ /*
*/ 2.83 , 3.15 \ /*
*/ 2.71 , 3.01 \ /*
*/ 2.62 , 2.89 \ /*
*/ 2.53 , 2.79 \ /*
*/ 2.46 , 2.70 \ /*
*/ 2.39 , 2.62 \ /*
*/ 2.33 , 2.55 \ /*
*/ 2.28 , 2.49 \ /*
*/ 2.23 , 2.43 \ /*
*/ 2.19 , 2.38 \ /*
*/ 2.15 , 2.33 \ /*
*/ 2.11 , 2.29 \ /*
*/ 2.08 , 2.25 \ /*
*/ 2.05 , 2.21 \ /*
*/ 2.02 , 2.18 \ /*
*/ 1.99 , 2.14 \ /*
*/ 1.97 , 2.11 \ /*
*/ 1.94 , 2.08 \ /*
*/ 1.92 , 2.06 \ /*
*/ 1.90 , 2.03 \ /*
*/ 1.88 , 2.01 \ /*
*/ 1.86 , 1.99 \ /*
*/ 1.85 , 1.97 \ /*
*/ 1.83 , 1.95 \ /*
*/ 1.81 , 1.93 \ /*
*/ 1.80 , 1.91 \ /*
*/ 1.79 , 1.89 \ /*
*/ 1.77 , 1.88 \ /*
*/ 1.76 , 1.86 \ /*
*/ 1.75 , 1.85 \ /*
*/ 1.74 , 1.83 \ /*
*/ 1.72 , 1.82 \ /*
*/ 1.71 , 1.81 \ /*
*/ 1.70 , 1.80 \ /*
*/ 1.69 , 1.78 \ /*
*/ 1.68 , 1.77 \ /*
*/ 1.67 , 1.76 \ /*
*/ 1.66 , 1.75 \ /*
*/ 1.66 , 1.74 \ /*
*/ 1.65 , 1.73 \ /*
*/ 1.64 , 1.72 \ /*
*/ 1.63 , 1.71 \ /*
*/ 1.62 , 1.70 \ /*
*/ 1.62 , 1.69 \ /*
*/ 1.61 , 1.69 \ /*
*/ 1.60 , 1.68 \ /*
*/ 1.60 , 1.67 \ /*
*/ 1.59 , 1.66 \ /*
*/ 1.58 , 1.65 \ /*
*/ 1.58 , 1.65 \ /*
*/ 1.57 , 1.64 \ /*
*/ 1.57 , 1.63 \ /*
*/ 1.56 , 1.63 \ /*
*/ 1.55 , 1.62 \ /*
*/ 1.55 , 1.61 \ /*
*/ 1.54 , 1.61 \ /*
*/ 1.54 , 1.60 \ /*
*/ 1.53 , 1.60 \ /*
*/ 1.53 , 1.59 \ /*
*/ 1.52 , 1.59 \ /*
*/ 1.52 , 1.58 \ /*
*/ 1.52 , 1.58 \ /*
*/ 1.51 , 1.57 \ /*
*/ 1.51 , 1.57 \ /*
*/ 1.50 , 1.56 \ /*
*/ 1.50 , 1.56 \ /*
*/ 1.49 , 1.55 \ /*
*/ 1.49 , 1.55 \ /*
*/ 1.49 , 1.54 \ /*
*/ 1.48 , 1.54 \ /*
*/ 1.48 , 1.53 \ /*
*/ 1.48 , 1.53 \ /*
*/ 1.47 , 1.53 \ /*
*/ 1.47 , 1.52 \ /*
*/ 1.46 , 1.52 \ /*
*/ 1.46 , 1.51 \ /*
*/ 1.46 , 1.51 \ /*
*/ 1.45 , 1.51 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.44 , 1.50 \ /*
*/ 1.44 , 1.49 \ /*
*/ 1.44 , 1.49 \ /*
*/ 1.44 , 1.49 \ /*
*/ 1.43 , 1.48 \ /*
*/ 1.43 , 1.48 \ /*
*/ 1.43 , 1.48 \ /*
*/ 1.42 , 1.48 \ /*
*/ 1.42 , 1.47 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="fullmax20" {
matrix input `temp' = ( /*
*/ 15.39 , . \ /*
*/ 8.16 , 9.41 \ /*
*/ 5.87 , 6.79 \ /*
*/ 4.75 , 5.47 \ /*
*/ 4.08 , 4.66 \ /*
*/ 3.64 , 4.13 \ /*
*/ 3.32 , 3.74 \ /*
*/ 3.08 , 3.45 \ /*
*/ 2.89 , 3.22 \ /*
*/ 2.74 , 3.03 \ /*
*/ 2.62 , 2.88 \ /*
*/ 2.51 , 2.76 \ /*
*/ 2.42 , 2.65 \ /*
*/ 2.35 , 2.56 \ /*
*/ 2.28 , 2.48 \ /*
*/ 2.22 , 2.40 \ /*
*/ 2.17 , 2.34 \ /*
*/ 2.12 , 2.28 \ /*
*/ 2.08 , 2.23 \ /*
*/ 2.04 , 2.19 \ /*
*/ 2.01 , 2.15 \ /*
*/ 1.98 , 2.11 \ /*
*/ 1.95 , 2.07 \ /*
*/ 1.92 , 2.04 \ /*
*/ 1.89 , 2.01 \ /*
*/ 1.87 , 1.98 \ /*
*/ 1.85 , 1.96 \ /*
*/ 1.83 , 1.93 \ /*
*/ 1.81 , 1.91 \ /*
*/ 1.79 , 1.89 \ /*
*/ 1.77 , 1.87 \ /*
*/ 1.76 , 1.85 \ /*
*/ 1.74 , 1.83 \ /*
*/ 1.73 , 1.82 \ /*
*/ 1.72 , 1.80 \ /*
*/ 1.70 , 1.79 \ /*
*/ 1.69 , 1.77 \ /*
*/ 1.68 , 1.76 \ /*
*/ 1.67 , 1.74 \ /*
*/ 1.66 , 1.73 \ /*
*/ 1.65 , 1.72 \ /*
*/ 1.64 , 1.71 \ /*
*/ 1.63 , 1.70 \ /*
*/ 1.62 , 1.69 \ /*
*/ 1.61 , 1.68 \ /*
*/ 1.60 , 1.67 \ /*
*/ 1.59 , 1.66 \ /*
*/ 1.58 , 1.65 \ /*
*/ 1.58 , 1.64 \ /*
*/ 1.57 , 1.63 \ /*
*/ 1.56 , 1.62 \ /*
*/ 1.56 , 1.62 \ /*
*/ 1.55 , 1.61 \ /*
*/ 1.54 , 1.60 \ /*
*/ 1.54 , 1.59 \ /*
*/ 1.53 , 1.59 \ /*
*/ 1.52 , 1.58 \ /*
*/ 1.52 , 1.57 \ /*
*/ 1.51 , 1.57 \ /*
*/ 1.51 , 1.56 \ /*
*/ 1.50 , 1.56 \ /*
*/ 1.50 , 1.55 \ /*
*/ 1.49 , 1.54 \ /*
*/ 1.49 , 1.54 \ /*
*/ 1.48 , 1.53 \ /*
*/ 1.48 , 1.53 \ /*
*/ 1.47 , 1.52 \ /*
*/ 1.47 , 1.52 \ /*
*/ 1.47 , 1.51 \ /*
*/ 1.46 , 1.51 \ /*
*/ 1.46 , 1.51 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.45 , 1.50 \ /*
*/ 1.45 , 1.49 \ /*
*/ 1.44 , 1.49 \ /*
*/ 1.44 , 1.48 \ /*
*/ 1.44 , 1.48 \ /*
*/ 1.43 , 1.48 \ /*
*/ 1.43 , 1.47 \ /*
*/ 1.43 , 1.47 \ /*
*/ 1.42 , 1.46 \ /*
*/ 1.42 , 1.46 \ /*
*/ 1.42 , 1.46 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.40 , 1.44 \ /*
*/ 1.40 , 1.44 \ /*
*/ 1.40 , 1.44 \ /*
*/ 1.40 , 1.44 \ /*
*/ 1.39 , 1.43 \ /*
*/ 1.39 , 1.43 \ /*
*/ 1.39 , 1.43 \ /*
*/ 1.39 , 1.42 \ /*
*/ 1.38 , 1.42 \ /*
*/ 1.38 , 1.42 \ /*
*/ 1.38 , 1.42 \ /*
*/ 1.38 , 1.41 \ /*
*/ 1.37 , 1.41 \ /*
*/ 1.37 , 1.41 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="fullmax30" {
matrix input `temp' = ( /*
*/ 12.76 , . \ /*
*/ 6.97 , 8.01 \ /*
*/ 5.11 , 5.88 \ /*
*/ 4.19 , 4.78 \ /*
*/ 3.64 , 4.12 \ /*
*/ 3.27 , 3.67 \ /*
*/ 3.00 , 3.35 \ /*
*/ 2.80 , 3.10 \ /*
*/ 2.64 , 2.91 \ /*
*/ 2.52 , 2.76 \ /*
*/ 2.41 , 2.63 \ /*
*/ 2.33 , 2.52 \ /*
*/ 2.25 , 2.43 \ /*
*/ 2.19 , 2.35 \ /*
*/ 2.13 , 2.29 \ /*
*/ 2.08 , 2.22 \ /*
*/ 2.04 , 2.17 \ /*
*/ 2.00 , 2.12 \ /*
*/ 1.96 , 2.08 \ /*
*/ 1.93 , 2.04 \ /*
*/ 1.90 , 2.01 \ /*
*/ 1.87 , 1.97 \ /*
*/ 1.84 , 1.94 \ /*
*/ 1.82 , 1.92 \ /*
*/ 1.80 , 1.89 \ /*
*/ 1.78 , 1.87 \ /*
*/ 1.76 , 1.84 \ /*
*/ 1.74 , 1.82 \ /*
*/ 1.73 , 1.80 \ /*
*/ 1.71 , 1.79 \ /*
*/ 1.70 , 1.77 \ /*
*/ 1.68 , 1.75 \ /*
*/ 1.67 , 1.74 \ /*
*/ 1.66 , 1.72 \ /*
*/ 1.64 , 1.71 \ /*
*/ 1.63 , 1.70 \ /*
*/ 1.62 , 1.68 \ /*
*/ 1.61 , 1.67 \ /*
*/ 1.60 , 1.66 \ /*
*/ 1.59 , 1.65 \ /*
*/ 1.58 , 1.64 \ /*
*/ 1.57 , 1.63 \ /*
*/ 1.57 , 1.62 \ /*
*/ 1.56 , 1.61 \ /*
*/ 1.55 , 1.60 \ /*
*/ 1.54 , 1.59 \ /*
*/ 1.54 , 1.59 \ /*
*/ 1.53 , 1.58 \ /*
*/ 1.52 , 1.57 \ /*
*/ 1.52 , 1.56 \ /*
*/ 1.51 , 1.56 \ /*
*/ 1.50 , 1.55 \ /*
*/ 1.50 , 1.54 \ /*
*/ 1.49 , 1.54 \ /*
*/ 1.49 , 1.53 \ /*
*/ 1.48 , 1.53 \ /*
*/ 1.48 , 1.52 \ /*
*/ 1.47 , 1.51 \ /*
*/ 1.47 , 1.51 \ /*
*/ 1.46 , 1.50 \ /*
*/ 1.46 , 1.50 \ /*
*/ 1.45 , 1.49 \ /*
*/ 1.45 , 1.49 \ /*
*/ 1.44 , 1.48 \ /*
*/ 1.44 , 1.48 \ /*
*/ 1.44 , 1.47 \ /*
*/ 1.43 , 1.47 \ /*
*/ 1.43 , 1.47 \ /*
*/ 1.42 , 1.46 \ /*
*/ 1.42 , 1.46 \ /*
*/ 1.42 , 1.45 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.41 , 1.45 \ /*
*/ 1.41 , 1.44 \ /*
*/ 1.40 , 1.44 \ /*
*/ 1.40 , 1.44 \ /*
*/ 1.40 , 1.43 \ /*
*/ 1.39 , 1.43 \ /*
*/ 1.39 , 1.43 \ /*
*/ 1.39 , 1.42 \ /*
*/ 1.39 , 1.42 \ /*
*/ 1.38 , 1.42 \ /*
*/ 1.38 , 1.41 \ /*
*/ 1.38 , 1.41 \ /*
*/ 1.37 , 1.41 \ /*
*/ 1.37 , 1.40 \ /*
*/ 1.37 , 1.40 \ /*
*/ 1.37 , 1.40 \ /*
*/ 1.36 , 1.40 \ /*
*/ 1.36 , 1.39 \ /*
*/ 1.36 , 1.39 \ /*
*/ 1.36 , 1.39 \ /*
*/ 1.36 , 1.38 \ /*
*/ 1.35 , 1.38 \ /*
*/ 1.35 , 1.38 \ /*
*/ 1.35 , 1.38 \ /*
*/ 1.35 , 1.37 \ /*
*/ 1.34 , 1.37 \ /*
*/ 1.34 , 1.37 \ /*
*/ 1.34 , 1.37 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="limlsize10" {
matrix input `temp' = ( /*
*/ 16.38 , . \ /*
*/ 8.68 , 7.03 \ /*
*/ 6.46 , 5.44 \ /*
*/ 5.44 , 4.72 \ /*
*/ 4.84 , 4.32 \ /*
*/ 4.45 , 4.06 \ /*
*/ 4.18 , 3.90 \ /*
*/ 3.97 , 3.78 \ /*
*/ 3.81 , 3.70 \ /*
*/ 3.68 , 3.64 \ /*
*/ 3.58 , 3.60 \ /*
*/ 3.50 , 3.58 \ /*
*/ 3.42 , 3.56 \ /*
*/ 3.36 , 3.55 \ /*
*/ 3.31 , 3.54 \ /*
*/ 3.27 , 3.55 \ /*
*/ 3.24 , 3.55 \ /*
*/ 3.20 , 3.56 \ /*
*/ 3.18 , 3.57 \ /*
*/ 3.21 , 3.58 \ /*
*/ 3.39 , 3.59 \ /*
*/ 3.57 , 3.60 \ /*
*/ 3.68 , 3.62 \ /*
*/ 3.75 , 3.64 \ /*
*/ 3.79 , 3.65 \ /*
*/ 3.82 , 3.67 \ /*
*/ 3.85 , 3.74 \ /*
*/ 3.86 , 3.87 \ /*
*/ 3.87 , 4.02 \ /*
*/ 3.88 , 4.12 \ /*
*/ 3.89 , 4.19 \ /*
*/ 3.89 , 4.24 \ /*
*/ 3.90 , 4.27 \ /*
*/ 3.90 , 4.31 \ /*
*/ 3.90 , 4.33 \ /*
*/ 3.90 , 4.36 \ /*
*/ 3.90 , 4.38 \ /*
*/ 3.90 , 4.39 \ /*
*/ 3.90 , 4.41 \ /*
*/ 3.90 , 4.43 \ /*
*/ 3.90 , 4.44 \ /*
*/ 3.90 , 4.45 \ /*
*/ 3.90 , 4.47 \ /*
*/ 3.90 , 4.48 \ /*
*/ 3.90 , 4.49 \ /*
*/ 3.90 , 4.50 \ /*
*/ 3.90 , 4.51 \ /*
*/ 3.90 , 4.52 \ /*
*/ 3.90 , 4.53 \ /*
*/ 3.90 , 4.54 \ /*
*/ 3.90 , 4.55 \ /*
*/ 3.90 , 4.56 \ /*
*/ 3.90 , 4.56 \ /*
*/ 3.90 , 4.57 \ /*
*/ 3.90 , 4.58 \ /*
*/ 3.90 , 4.59 \ /*
*/ 3.90 , 4.59 \ /*
*/ 3.90 , 4.60 \ /*
*/ 3.90 , 4.61 \ /*
*/ 3.90 , 4.61 \ /*
*/ 3.90 , 4.62 \ /*
*/ 3.90 , 4.62 \ /*
*/ 3.90 , 4.63 \ /*
*/ 3.90 , 4.63 \ /*
*/ 3.89 , 4.64 \ /*
*/ 3.89 , 4.64 \ /*
*/ 3.89 , 4.64 \ /*
*/ 3.89 , 4.65 \ /*
*/ 3.89 , 4.65 \ /*
*/ 3.89 , 4.65 \ /*
*/ 3.89 , 4.66 \ /*
*/ 3.89 , 4.66 \ /*
*/ 3.89 , 4.66 \ /*
*/ 3.89 , 4.66 \ /*
*/ 3.88 , 4.66 \ /*
*/ 3.88 , 4.66 \ /*
*/ 3.88 , 4.66 \ /*
*/ 3.88 , 4.66 \ /*
*/ 3.88 , 4.66 \ /*
*/ 3.88 , 4.66 \ /*
*/ 3.88 , 4.66 \ /*
*/ 3.87 , 4.66 \ /*
*/ 3.87 , 4.66 \ /*
*/ 3.87 , 4.66 \ /*
*/ 3.87 , 4.66 \ /*
*/ 3.87 , 4.66 \ /*
*/ 3.86 , 4.65 \ /*
*/ 3.86 , 4.65 \ /*
*/ 3.86 , 4.65 \ /*
*/ 3.86 , 4.64 \ /*
*/ 3.85 , 4.64 \ /*
*/ 3.85 , 4.64 \ /*
*/ 3.85 , 4.63 \ /*
*/ 3.85 , 4.63 \ /*
*/ 3.84 , 4.62 \ /*
*/ 3.84 , 4.62 \ /*
*/ 3.84 , 4.61 \ /*
*/ 3.84 , 4.60 \ /*
*/ 3.83 , 4.60 \ /*
*/ 3.83 , 4.59 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="limlsize15" {
matrix input `temp' = ( /*
*/ 8.96 , . \ /*
*/ 5.33 , 4.58 \ /*
*/ 4.36 , 3.81 \ /*
*/ 3.87 , 3.39 \ /*
*/ 3.56 , 3.13 \ /*
*/ 3.34 , 2.95 \ /*
*/ 3.18 , 2.83 \ /*
*/ 3.04 , 2.73 \ /*
*/ 2.93 , 2.66 \ /*
*/ 2.84 , 2.60 \ /*
*/ 2.76 , 2.55 \ /*
*/ 2.69 , 2.52 \ /*
*/ 2.63 , 2.48 \ /*
*/ 2.57 , 2.46 \ /*
*/ 2.52 , 2.44 \ /*
*/ 2.48 , 2.42 \ /*
*/ 2.44 , 2.41 \ /*
*/ 2.41 , 2.40 \ /*
*/ 2.37 , 2.39 \ /*
*/ 2.34 , 2.38 \ /*
*/ 2.32 , 2.38 \ /*
*/ 2.29 , 2.37 \ /*
*/ 2.27 , 2.37 \ /*
*/ 2.25 , 2.37 \ /*
*/ 2.24 , 2.37 \ /*
*/ 2.22 , 2.38 \ /*
*/ 2.21 , 2.38 \ /*
*/ 2.20 , 2.38 \ /*
*/ 2.19 , 2.39 \ /*
*/ 2.18 , 2.39 \ /*
*/ 2.19 , 2.40 \ /*
*/ 2.22 , 2.41 \ /*
*/ 2.33 , 2.42 \ /*
*/ 2.40 , 2.42 \ /*
*/ 2.45 , 2.43 \ /*
*/ 2.48 , 2.44 \ /*
*/ 2.50 , 2.45 \ /*
*/ 2.52 , 2.54 \ /*
*/ 2.53 , 2.55 \ /*
*/ 2.54 , 2.66 \ /*
*/ 2.55 , 2.73 \ /*
*/ 2.56 , 2.78 \ /*
*/ 2.57 , 2.82 \ /*
*/ 2.57 , 2.85 \ /*
*/ 2.58 , 2.87 \ /*
*/ 2.58 , 2.89 \ /*
*/ 2.58 , 2.91 \ /*
*/ 2.59 , 2.92 \ /*
*/ 2.59 , 2.93 \ /*
*/ 2.59 , 2.94 \ /*
*/ 2.59 , 2.95 \ /*
*/ 2.59 , 2.96 \ /*
*/ 2.60 , 2.97 \ /*
*/ 2.60 , 2.98 \ /*
*/ 2.60 , 2.98 \ /*
*/ 2.60 , 2.99 \ /*
*/ 2.60 , 2.99 \ /*
*/ 2.60 , 3.00 \ /*
*/ 2.60 , 3.00 \ /*
*/ 2.60 , 3.01 \ /*
*/ 2.60 , 3.01 \ /*
*/ 2.60 , 3.02 \ /*
*/ 2.61 , 3.02 \ /*
*/ 2.61 , 3.02 \ /*
*/ 2.61 , 3.03 \ /*
*/ 2.61 , 3.03 \ /*
*/ 2.61 , 3.03 \ /*
*/ 2.61 , 3.03 \ /*
*/ 2.61 , 3.04 \ /*
*/ 2.61 , 3.04 \ /*
*/ 2.61 , 3.04 \ /*
*/ 2.60 , 3.04 \ /*
*/ 2.60 , 3.04 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.60 , 3.05 \ /*
*/ 2.59 , 3.05 \ /*
*/ 2.59 , 3.05 \ /*
*/ 2.59 , 3.05 \ /*
*/ 2.59 , 3.05 \ /*
*/ 2.59 , 3.05 \ /*
*/ 2.59 , 3.04 \ /*
*/ 2.58 , 3.04 \ /*
*/ 2.58 , 3.04 \ /*
*/ 2.58 , 3.04 \ /*
*/ 2.58 , 3.04 \ /*
*/ 2.58 , 3.03 \ /*
*/ 2.57 , 3.03 \ /*
*/ 2.57 , 3.03 \ /*
*/ 2.57 , 3.03 \ /*
*/ 2.57 , 3.02 \ /*
*/ 2.56 , 3.02 \ /*
*/ 2.56 , 3.02 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="limlsize20" {
matrix input `temp' = ( /*
*/ 6.66 , . \ /*
*/ 4.42 , 3.95 \ /*
*/ 3.69 , 3.32 \ /*
*/ 3.30 , 2.99 \ /*
*/ 3.05 , 2.78 \ /*
*/ 2.87 , 2.63 \ /*
*/ 2.73 , 2.52 \ /*
*/ 2.63 , 2.43 \ /*
*/ 2.54 , 2.36 \ /*
*/ 2.46 , 2.30 \ /*
*/ 2.40 , 2.25 \ /*
*/ 2.34 , 2.21 \ /*
*/ 2.29 , 2.17 \ /*
*/ 2.25 , 2.14 \ /*
*/ 2.21 , 2.11 \ /*
*/ 2.18 , 2.09 \ /*
*/ 2.14 , 2.07 \ /*
*/ 2.11 , 2.05 \ /*
*/ 2.09 , 2.03 \ /*
*/ 2.06 , 2.02 \ /*
*/ 2.04 , 2.01 \ /*
*/ 2.02 , 1.99 \ /*
*/ 2.00 , 1.98 \ /*
*/ 1.98 , 1.98 \ /*
*/ 1.96 , 1.97 \ /*
*/ 1.95 , 1.96 \ /*
*/ 1.93 , 1.96 \ /*
*/ 1.92 , 1.95 \ /*
*/ 1.90 , 1.95 \ /*
*/ 1.89 , 1.95 \ /*
*/ 1.88 , 1.94 \ /*
*/ 1.87 , 1.94 \ /*
*/ 1.86 , 1.94 \ /*
*/ 1.85 , 1.94 \ /*
*/ 1.84 , 1.94 \ /*
*/ 1.83 , 1.94 \ /*
*/ 1.82 , 1.94 \ /*
*/ 1.81 , 1.95 \ /*
*/ 1.81 , 1.95 \ /*
*/ 1.80 , 1.95 \ /*
*/ 1.79 , 1.95 \ /*
*/ 1.79 , 1.96 \ /*
*/ 1.78 , 1.96 \ /*
*/ 1.78 , 1.97 \ /*
*/ 1.80 , 1.97 \ /*
*/ 1.87 , 1.98 \ /*
*/ 1.92 , 1.98 \ /*
*/ 1.95 , 1.99 \ /*
*/ 1.97 , 2.00 \ /*
*/ 1.99 , 2.00 \ /*
*/ 2.00 , 2.01 \ /*
*/ 2.01 , 2.09 \ /*
*/ 2.02 , 2.11 \ /*
*/ 2.03 , 2.18 \ /*
*/ 2.04 , 2.23 \ /*
*/ 2.04 , 2.27 \ /*
*/ 2.05 , 2.29 \ /*
*/ 2.05 , 2.31 \ /*
*/ 2.06 , 2.33 \ /*
*/ 2.06 , 2.34 \ /*
*/ 2.07 , 2.35 \ /*
*/ 2.07 , 2.36 \ /*
*/ 2.07 , 2.37 \ /*
*/ 2.08 , 2.38 \ /*
*/ 2.08 , 2.39 \ /*
*/ 2.08 , 2.39 \ /*
*/ 2.08 , 2.40 \ /*
*/ 2.09 , 2.40 \ /*
*/ 2.09 , 2.41 \ /*
*/ 2.09 , 2.41 \ /*
*/ 2.09 , 2.41 \ /*
*/ 2.09 , 2.42 \ /*
*/ 2.09 , 2.42 \ /*
*/ 2.09 , 2.42 \ /*
*/ 2.09 , 2.43 \ /*
*/ 2.10 , 2.43 \ /*
*/ 2.10 , 2.43 \ /*
*/ 2.10 , 2.43 \ /*
*/ 2.10 , 2.44 \ /*
*/ 2.10 , 2.44 \ /*
*/ 2.10 , 2.44 \ /*
*/ 2.10 , 2.44 \ /*
*/ 2.10 , 2.44 \ /*
*/ 2.09 , 2.44 \ /*
*/ 2.09 , 2.44 \ /*
*/ 2.09 , 2.45 \ /*
*/ 2.09 , 2.45 \ /*
*/ 2.09 , 2.45 \ /*
*/ 2.09 , 2.45 \ /*
*/ 2.09 , 2.45 \ /*
*/ 2.09 , 2.45 \ /*
*/ 2.09 , 2.45 \ /*
*/ 2.08 , 2.45 \ /*
*/ 2.08 , 2.45 \ /*
*/ 2.08 , 2.45 \ /*
*/ 2.08 , 2.45 \ /*
*/ 2.08 , 2.45 \ /*
*/ 2.07 , 2.44 \ /*
*/ 2.07 , 2.44 \ /*
*/ 2.07 , 2.44 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
if "`type'"=="limlsize25" {
matrix input `temp' = ( /*
*/ 5.53 , . \ /*
*/ 3.92 , 3.63 \ /*
*/ 3.32 , 3.09 \ /*
*/ 2.98 , 2.79 \ /*
*/ 2.77 , 2.60 \ /*
*/ 2.61 , 2.46 \ /*
*/ 2.49 , 2.35 \ /*
*/ 2.39 , 2.27 \ /*
*/ 2.32 , 2.20 \ /*
*/ 2.25 , 2.14 \ /*
*/ 2.19 , 2.09 \ /*
*/ 2.14 , 2.05 \ /*
*/ 2.10 , 2.02 \ /*
*/ 2.06 , 1.99 \ /*
*/ 2.03 , 1.96 \ /*
*/ 2.00 , 1.93 \ /*
*/ 1.97 , 1.91 \ /*
*/ 1.94 , 1.89 \ /*
*/ 1.92 , 1.87 \ /*
*/ 1.90 , 1.86 \ /*
*/ 1.88 , 1.84 \ /*
*/ 1.86 , 1.83 \ /*
*/ 1.84 , 1.81 \ /*
*/ 1.83 , 1.80 \ /*
*/ 1.81 , 1.79 \ /*
*/ 1.80 , 1.78 \ /*
*/ 1.78 , 1.77 \ /*
*/ 1.77 , 1.77 \ /*
*/ 1.76 , 1.76 \ /*
*/ 1.75 , 1.75 \ /*
*/ 1.74 , 1.75 \ /*
*/ 1.73 , 1.74 \ /*
*/ 1.72 , 1.73 \ /*
*/ 1.71 , 1.73 \ /*
*/ 1.70 , 1.73 \ /*
*/ 1.69 , 1.72 \ /*
*/ 1.68 , 1.72 \ /*
*/ 1.67 , 1.71 \ /*
*/ 1.67 , 1.71 \ /*
*/ 1.66 , 1.71 \ /*
*/ 1.65 , 1.71 \ /*
*/ 1.65 , 1.71 \ /*
*/ 1.64 , 1.70 \ /*
*/ 1.63 , 1.70 \ /*
*/ 1.63 , 1.70 \ /*
*/ 1.62 , 1.70 \ /*
*/ 1.62 , 1.70 \ /*
*/ 1.61 , 1.70 \ /*
*/ 1.61 , 1.70 \ /*
*/ 1.61 , 1.70 \ /*
*/ 1.60 , 1.70 \ /*
*/ 1.60 , 1.70 \ /*
*/ 1.59 , 1.70 \ /*
*/ 1.59 , 1.70 \ /*
*/ 1.59 , 1.70 \ /*
*/ 1.58 , 1.70 \ /*
*/ 1.58 , 1.71 \ /*
*/ 1.58 , 1.71 \ /*
*/ 1.57 , 1.71 \ /*
*/ 1.59 , 1.71 \ /*
*/ 1.60 , 1.71 \ /*
*/ 1.63 , 1.72 \ /*
*/ 1.65 , 1.72 \ /*
*/ 1.67 , 1.72 \ /*
*/ 1.69 , 1.72 \ /*
*/ 1.70 , 1.76 \ /*
*/ 1.71 , 1.81 \ /*
*/ 1.72 , 1.87 \ /*
*/ 1.73 , 1.91 \ /*
*/ 1.74 , 1.94 \ /*
*/ 1.74 , 1.96 \ /*
*/ 1.75 , 1.98 \ /*
*/ 1.75 , 1.99 \ /*
*/ 1.76 , 2.01 \ /*
*/ 1.76 , 2.02 \ /*
*/ 1.77 , 2.03 \ /*
*/ 1.77 , 2.04 \ /*
*/ 1.78 , 2.04 \ /*
*/ 1.78 , 2.05 \ /*
*/ 1.78 , 2.06 \ /*
*/ 1.79 , 2.06 \ /*
*/ 1.79 , 2.07 \ /*
*/ 1.79 , 2.07 \ /*
*/ 1.79 , 2.08 \ /*
*/ 1.80 , 2.08 \ /*
*/ 1.80 , 2.09 \ /*
*/ 1.80 , 2.09 \ /*
*/ 1.80 , 2.09 \ /*
*/ 1.80 , 2.09 \ /*
*/ 1.80 , 2.10 \ /*
*/ 1.80 , 2.10 \ /*
*/ 1.80 , 2.10 \ /*
*/ 1.80 , 2.10 \ /*
*/ 1.80 , 2.10 \ /*
*/ 1.80 , 2.11 \ /*
*/ 1.80 , 2.11 \ /*
*/ 1.80 , 2.11 \ /*
*/ 1.80 , 2.11 \ /*
*/ 1.80 , 2.11 \ /*
*/ 1.80 , 2.11 )
if `k2'<=100 & `nendog'<=2 {
scalar `cv'=`temp'[`k2',`nendog']
}
}
return scalar cv=`cv'
end
exit
********************************** VERSION COMMENTS **********************************
* 1.0.2: add logic for reg3. Sargan test
* 1.0.3: add prunelist to ensure that count of excluded exogeneous is correct
* 1.0.4: revise option to exog(), allow included exog to be specified as well
* 1.0.5: switch from reg3 to regress, many options and output changes
* 1.0.6: fixed treatment of nocons in Sargan and C-stat, and corrected problems
* relating to use of nocons combined with a constant as an IV
* 1.0.7: first option reports F-test of excluded exogenous; prunelist bug fix
* 1.0.8: dropped prunelist and switched to housekeeping of variable lists
* 1.0.9: added collinearity checks; C-stat calculated with recursive call;
* added ffirst option to report only F-test of excluded exogenous
* from 1st stage regressions
* 1.0.10: 1st stage regressions also report partial R2 of excluded exogenous
* 1.0.11: complete rewrite of collinearity approach - no longer uses calls to
* _rmcoll, does not track specific variables dropped; prunelist removed
* 1.0.12: reorganised display code and saved results to enable -replay()-
* 1.0.13: -robust- and -cluster- now imply -small-
* 1.0.14: fixed hascons bug; removed ivreg predict fn (it didn't work); allowed
* robust and cluster with z stats and correct dofs
* 1.0.15: implemented robust Sargan stat; changed to only F-stat, removed chi-sq;
* removed exog option (only orthog works)
* 1.0.16: added clusterised Sargan stat; robust Sargan handles collinearities;
* predict now works with standard SE options plus resids; fixed orthog()
* so it accepts time series operators etc.
* 1.0.17: fixed handling of weights. fw, aw, pw & iw all accepted.
* 1.0.18: fixed bug in robust Sargan code relating to time series variables.
* 1.0.19: fixed bugs in reporting ranks of X'X and Z'Z
* fixed bug in reporting presence of constant
* 1.0.20: added GMM option and replaced robust Sargan with (equivalent) J;
* added saved statistics of 1st stage regressions
* 1.0.21: added Cragg HOLS estimator, including allowing empty endog list;
* -regress- syntax now not allowed; revised code searching for "_cons"
* 1.0.22: modified cluster output message; fixed bug in replay for Sargan/Hansen stat;
* exactly identified Sargan/Hansen now exactly zero and p-value not saved as e();
* cluster multiplier changed to 1 (from buggy multiplier), in keeping with
* eg Wooldridge 2002 p. 193.
* 1.0.23: fixed orthog option to prevent abort when restricted equation is underid.
* 1.0.24: fixed bug if 1st stage regressions yielded missing values for saving in e().
* 1.0.25: Added Shea version of partial R2
* 1.0.26: Replaced Shea algorithm with Godfrey algorithm
* 1.0.27: Main call to regress is OLS form if OLS or HOLS is specified; error variance
* in Sargan and C statistics use small-sample adjustment if -small- option is
* specified; dfn of S matrix now correctly divided by sample size
* 1.0.28: HAC covariance estimation implemented
* Symmetrize all matrices before calling syminv
* Added hack to catch F stats that ought to be missing but actually have a
* huge-but-not-missing value
* Fixed dof of F-stat - was using rank of ZZ, should have used rank of XX (couldn't use df_r
* because it isn't always saved. This is because saving df_r triggers small stats
* (t and F) even when -post- is called without dof() option, hence df_r saved only
* with -small- option and hence a separate saved macro Fdf2 is needed.
* Added rankS to saved macros
* Fixed trap for "no regressors specified"
* Added trap to catch gmm option with no excluded instruments
* Allow OLS syntax (no endog or excluded IVs specified)
* Fixed error messages and traps for rank-deficient robust cov matrix; includes
* singleton dummy possibility
* Capture error if posting estimated VCV that isn't pos def and report slightly
* more informative error message
* Checks 3 variable lists (endo, inexog, exexog) separately for collinearities
* Added AC (autocorrelation-consistent but conditionally-homoskedastic) option
* Sargan no longer has small-sample correction if -small- option
* robust, cluster, AC, HAC all passed on to first-stage F-stat
* bw must be < T
* 1.0.29 -orthog- also displays Hansen-Sargan of unrestricted equation
* Fixed collinearity check to include nocons as well as hascons
* Fixed small bug in Godfrey-Shea code - macros were global rather than local
* Fixed larger bug in Godfrey-Shea code - was using mixture of sigma-squares from IV and OLS
* with and without small-sample corrections
* Added liml and kclass
* 1.0.30 Changed order of insts macro to match saved matrices S and W
* 2.0.00 Collinearities no longer -qui-
* List of instruments tested in -orthog- option prettified
* 2.0.01 Fixed handling of nocons with no included exogenous, including LIML code
* 2.0.02 Allow C-test if unrestricted equation is just-identified. Implemented by
* saving Hansen-Sargan dof as = 0 in e() if just-identified.
* 2.0.03 Added score() option per latest revision to official ivreg
* 2.0.04 Changed score() option to pscore() per new official ivreg
* 2.0.05 Fixed est hold bug in first-stage regressions
* Fixed F-stat finite sample adjustment with cluster option to match official Stata
* Fixed F-stat so that it works with hascons (collinearity with constant is removed)
* Fixed bug in F-stat code - wasn't handling failed posting of vcv
* No longer allows/ignores nonsense options
* 2.0.06 Modified lsStop to sync with official ivreg 5.1.3
* 2.0.07a Working version of CUE option
* Added sortpreserve, ivar and tvar options
* Fixed smalls bug in calculation of T for AC/HAC - wasn't using the last ob
* in QS kernel, and didn't take account of possible dropped observations
* 2.0.07b Fixed macro bug that truncated long varlists
* 2.0.07c Added dof option.
* Changed display of RMSE so that more digits are displayed (was %8.1g)
* Fixed small bug where cstat was local macro and should have been scalar
* Fixed bug where C stat failed with cluster. NB: wmatrix option and cluster are not compatible!
* 2.0.7d Fixed bug in dof option
* 2.1.0 Added first-stage identification, weak instruments, and redundancy stats
* 2.1.01 Tidying up cue option checks, reporting of cue in output header, etc.
* 2.1.02 Used Poskitt-Skeels (2002) result that C-D eval = cceval / (1-cceval)
* 2.1.03 Added saved lists of separate included and excluded exogenous IVs
* 2.1.04 Added Anderson-Rubin test of signif of endog regressors
* 2.1.05 Fix minor bugs relating to cluster and new first-stage stats
* 2.1.06 Fix bug in cue: capture estimates hold without corresponding capture on estimates unhold
* 2.1.07 Minor fix to ereturn local wexp, promote to version 8.2
* 2.1.08 Added dofminus option, removed dof option. Added A-R test p-values to e().
* Minor bug fix to A-R chi2 test - was N chi2, should have been N-L chi2.
* Changed output to remove potentially misleading refs to N-L etc.
* Bug fix to rhs count - sometimes regressors could have exact zero coeffs
* Bug fix related to cluster - if user omitted -robust-, orthog would use Sargan and not J
* Changed output of Shea R2 to make clearer that F and p-values do not refer to it
* Improved handling of collinearites to check across inexog, exexog and endo lists
* Total weight statement moved to follow summ command
* Added traps to catch errors if no room to save temporary estimations with _est hold
* Added -savefirst- option. Removed -hascons-, now synonymous with -nocons-.
* 2.1.09 Fixes to dof option with cluster so it no longer mimics incorrect areg behavior
* Local ivreg2_cmd to allow testing under name ivreg2
* If wmatrix supplied, used (previously not used if non-robust sargan stat generated)
* Allowed OLS using (=) syntax (empty endo and exexog lists)
* Clarified error message when S matrix is not of full rank
* cdchi2p, ardf, ardf_r added to saved macros
* first and ffirst replay() options; DispFirst and DispFFirst separately codes 1st stage output
* Added savefprefix, macro with saved first-stage equation names.
* Added version option.
* Added check for duplicate variables to collinearity checks
* Rewrote/simplified Godfrey-Shea partial r2 code
* 2.1.10 Added NOOUTput option
* Fixed rf bug so that first does not trigger unnecessary saved rf
* Fixed cue bug - was not starting with robust 2-step gmm if robust/cluster
* 2.1.11 Dropped incorrect/misleading dofminus adjustments in first-stage output summary
* 2.1.12 Collinearity check now checks across inexog/exexog/endog simultaneously
* 2.1.13 Added check to catch failed first-stage regressions
* Fixed misleading failed C-stat message
* 2.1.14 Fixed mishandling of missing values in AC (non-robust) block
* 2.1.15 Fixed bug in RF - was ignoring weights
* Added -endog- option
* Save W matrix for all cases; ensured copy is posted with wmatrix option so original isn't zapped
* Fixed cue bug - with robust, was entering IV block and overwriting correct VCV
* 2.1.16 Added -fwl- option
* Saved S is now robust cov matrix of orthog conditions if robust, whereas W is possibly non-robust
* weighting matrix used by estmator. inv(S)=W if estimator is efficient GMM.
* Removed pscore option (dropped by official ivreg).
* Fixed bug where -post- would fail because of missing values in vcv
* Remove hascons as synonym for nocons
* OLS now outputs 2nd footer with variable lists
* 2.1.17 Reorganization of code
* Added ll() macro
* Fixed N bug where weights meant a non-integer ob count that was rounded down
* Fixed -fwl- option so it correctly handles weights (must include when partialling-out)
* smatrix option takes over from wmatrix option. Consistent treatment of both.
* Saved smatrix and wmatrix now differ in case of inefficient GMM.
* Added title() and subtitle() options.
* b0 option returns a value for the Sargan/J stat even if exactly id'd.
* (Useful for S-stat = value of GMM objective function.)
* HAC and AC now allowed with LIML and k-class.
* Collinearity improvements: bug fixed because collinearity was mistakenly checked across
* inexog/exexog/endog simultaneously; endog predicted exactly by IVs => reclassified as inexog;
* _rmcollright enforces inexog>endo>exexog priority for collinearities, if Stata 9.2 or later.
* K-class, LIML now report Sargan and J. C-stat based on Sargan/J. LIML reports AR if homosked.
* nb: can always easily get a C-stat for LIML based on diff of two AR stats.
* Always save Sargan-Hansen as e(j); also save as e(sargan) if homoskedastic.
* Added Stock-Watson robust SEs options sw()
* 2.1.18 Added Cragg-Donald-Stock-Yogo weak ID statistic critical values to main output
* Save exexog_ct, inexog_ct and endog_ct as macros
* Stock-Watson robust SEs now assume ivar is group variable
* Option -sw- is standard SW. Option -swpsd- is PSD version a la page 6 point 10.
* Added -noid- option. Suppresses all first-stage and identification statistics.
* Internal calls to ivreg2 use noid option.
* Added hyperlinks to ivreg2.hlp and helpfile argument to display routines to enable this.
* 2.1.19 Added matrix rearrangement and checks for smatrix and wmatrix options
* Recursive calls to cstat simplified - no matrix rearrangement or separate robust/nonrobust needed
* Reintroduced weak ID stats to ffirst output
* Added robust ID stats to ffirst output for case of single endogenous regressor
* Fixed obscure bug in reporting 1st stage partial r2 - would report zero if no included exogenous vars
* Removed "HOLS" in main output (misleading if, e.g., estimation is AC but not HAC)
* Removed "ML" in main output if no endogenous regressors - now all ML is labelled LIML
* model=gmm is now model=gmm2s; wmatrix estimation is model=gmm
* wmatrix relates to gmm estimator; smatrix relates to gmm var-cov matrix; b0 behavior equiv to wmatrix
* b0 option implies nooutput and noid options
* Added nocollin option to skip collinearity checks
* Fixed minor display bug in ffirst output for endog vars with varnames > 12 characters
* Fixed bug in saved rf and first-stage results for vars with long varnames; uses permname
* Fixed bug in model df - had counted RHS, now calculates rank(V) since latter may be rank-deficient
* Rank of V now saved as macro rankV
* fwl() now allows partialling-out of just constant with _cons
* Added Stock-Wright S statistic (but adds overhead - calls preserve)
* Properties now include svyj.
* Noted only: fwl bug doesn't allow time-series operators.
* 2.1.20 Fixed Stock-Wright S stat bug - didn't allow time-series operators
* 2.1.21 Fixed Stock-Wright S stat to allow for no exog regressors cases
* 2.1.22 Misc fixes. Fixed bug in AC with aweights; was weighting zi'zi but not ei'ei.
* Fixed bug in AC; need to clear variable vt1 at start of loop
* If iweights, Nprec (#obs with precision) rounded to nearest integer to mimic official Stata treatment