*! ivreset 1.0.08 4Feb2007
*! author mes
* Z are instruments (L of them) and X are regressors (K of them)
* 1.0.04 Fixed small bug in check for xtmodel
* Fixed dof check in xtivreg2
* 1.0.05 Changed rescaling so that squares, cubes, etc are created then rescaled
* 1.0.06 Removed rescaling, switched to -orthog-
* Removed support for official ivreg
* Fixed small bug in failed cstat reporting
* 1.0.07 Added trap to catch usage after ivreg2 with fwl
* 1.0.08 Refined trap
program define ivreset, rclass sortpreserve
version 8.0
local version 1.0.8
syntax [, POLYnomial(integer 2) RForm cstat small]
if "`e(cmd)'" != "ivreg2" & "`e(cmd)'" != "regress" & /*
*/ "`e(cmd)'" != "ivreg3" & "`e(cmd)'" != "xtivreg2" {
error 301
}
if "`e(cmd)'" == "xtivreg2" & "`e(xtmodel)'" ~= "fe" {
error 301
}
if "`e(fwlcons)'" != "" {
di in r "ivreset not allowed after ivreg2 with fwl option"
error 499
}
if "`cstat'" != "" {
* Check that ivreg2 is installed when cstat option is requested
capture findfile ivreg2.ado
if _rc==601 {
di as err "Error: installed ivreg2 required for cstat option"
error 601
}
}
tempvar touse N yhat yhat2 yhat3 yhat4
tempname regest b
* Default is to use Pesaran-Smith optimal forecast of y
if "`rform'" == "" {
local msg0 "Ramsey/Pesaran-Taylor RESET test"
local msg2 "fitted value of y (X-hat*beta-hat)"
}
else {
local msg0 "Ramsey/Pagan-Hall RESET test"
local msg2 "reduced form prediction of y"
}
if "`cstat'" == "" {
local msg3 "Wald"
}
else {
local msg3 "C (GMM-distance)"
}
if "`e(vcetype)'"=="Robust" {
local robust "robust"
local msg3h "heteroskedastic-"
}
if "`e(clustvar)'"~="" {
local clopt "cluster(`e(clustvar)')"
local msg3c "cluster-"
}
if "`e(bw)'"~="" {
local bwopt "bw(`e(bw)')"
local kernopt "kernel(`e(kernel)')"
local msg3a "autocorrelation-"
}
if "`e(wtype)'" != "" {
local wtexp `"[`e(wtype)'`e(wexp)']"'
}
gen `touse'=e(sample)
mat `b' = e(b)
local rhs : colnames `b'
local rhsct : word count `rhs'
local depvar "`e(depvar)'"
local df_m = e(df_m)
if "`e(cmd)'" != "regress" {
* Block for all IV commands
local endo "`e(instd)'"
local insts "`e(insts)'"
foreach vn of local rhs {
local tempvlist : subinstr local insts "`vn'" "`vn'" , /*
*/ word count(local isiv)
if `isiv'==1 {
local inexog "`inexog' `vn'"
}
}
foreach vn of local insts {
local tempvlist : subinstr local rhs "`vn'" "`vn'" , /*
*/ word count(local isregressor)
if `isregressor'==0 {
local exexog "`exexog' `vn'"
}
}
* If endo is empty, it's OLS or HOLS, and if so, ignore exexog as well
if "`endo'"=="" {
local exexog
}
if e(cons)~=1 {
local consflag 0
local consopt "nocons"
}
else {
local consflag 1
}
}
else {
* Block for simple regress
local endo
local exexog
local inexog : subinstr local rhs "_cons" "", word count(local consflag)
if `consflag'==0 {
local consopt "nocons"
}
}
* If no endogenous regressors, it's just a simple RESET test
if "`endo'" == "" {
local msg0 "Ramsey RESET test"
local msg2 "fitted value of y (X*beta-hat)"
}
* Demeaning block for xtivreg2
if "`e(cmd)'" == "xtivreg2" {
preserve
tempvar ivar T_i depvar2
qui gen double `depvar2' = `depvar'
qui gen `ivar' = `e(ivar)'
sort `ivar' `touse'
qui by `ivar' `touse': gen long `T_i' = _N if _n==_N & `touse'
qui count if `T_i' < .
* N_g minus 1 is additional degrees of freedom absorbed by the extra dummies
local N_g=r(N)
local allvars "`depvar' `inexog' `endo' `exexog'"
qui foreach var of local allvars {
tempname `var'_m
by `ivar' `touse' : gen double ``var'_m'=sum(`var')/_N if `touse'
by `ivar' `touse' : replace ``var'_m'=``var'_m'[_N] if `touse' & _n<_N
by `ivar' `touse' : replace `var'=`var'-``var'_m'[_N] if `touse'
}
* Small degrees of freedom = N minus #regressors minus #fixed effects (+constant)
* Large = N minus #fixed effects
qui sum `depvar' if `touse'
local N=r(N)
* Use only large dof, not small. Subtract full number of fixed effects `N_g'
local dof = `N' - `N_g'
* nocons not part of xtopts - caught below
local xtopts "dofminus(`N_g')"
}
if "`rform'" == "" {
* Code for Pesaran-Smith test using "predicted" values "yhat"=xhat*Bhat (NOT x*Bhat)
* Generate predicted values of endog regressors
* In special case of no endog regressors (OLS or HOLS), exexog has also been set to empty
* and yhat is just the usual OLS/HOLS yhat
foreach vn of local endo {
capture _estimates hold `regest', restore
qui regress `vn' `inexog' `exexog' `wtexp' if `touse', `consopt'
tempvar xh
qui predict double `xh' if `touse', xb
capture _estimates unhold `regest'
local endohat "`endohat' `xh'"
}
local rhshat "`endohat' `inexog'"
mat `b' = e(b)
if `consflag' {
local rhshat "`rhshat' _cons"
}
matrix colnames `b' = `rhshat'
matrix score double `yhat' = `b' if `touse'
}
if "`rform'" != "" {
* Code for Pagan-Hall test using reduced form predictions
* In special case of no endog regressors (OLS or HOLS), exexog has also been set to empty
* and yhat is just the usual OLS/HOLS yhat
capture _estimates hold `regest', restore
qui regress `depvar' `inexog' `exexog' `wtexp', `consopt'
qui predict double `yhat', xb
capture _estimates unhold `regest'
}
* Add mean back to yhat if xtivreg2
qui if "`e(cmd)'" == "xtivreg2" {
tempname depvar_m
by `ivar' `touse' : gen double `depvar_m'=sum(`depvar2')/_N if `touse'
by `ivar' `touse' : replace `depvar_m'=`depvar_m'[_N] if `touse' & _n<_N
by `ivar' `touse' : replace `yhat' =`yhat' +`depvar_m'[_N] if `touse'
}
* Generate squares, and cube/4th powers if requested. Always need yhat^2.
* Orthogonalize if order>3.
qui gen double `yhat2'=`yhat'^2
if `polynomial'==2 {
local yhats "`yhat2'"
local msg1 "square of "
}
if `polynomial'==3 {
tempname yhat2o yhat3o
qui gen double `yhat3'=`yhat'^3
orthog `yhat2' `yhat3' if `touse', gen(`yhat2o' `yhat3o')
qui replace `yhat2'=`yhat2o'
qui replace `yhat3'=`yhat3o'
local yhats "`yhat2' `yhat3'"
local msg1 "square and cube of "
}
if `polynomial'==4 {
tempname yhat2o yhat3o yhat4o
qui gen double `yhat3'=`yhat'^3
qui gen double `yhat4'=`yhat'^4
orthog `yhat2' `yhat3' `yhat4' if `touse', gen(`yhat2o' `yhat3o' `yhat4o')
qui replace `yhat2'=`yhat2o'
qui replace `yhat3'=`yhat3o'
qui replace `yhat4'=`yhat4o'
local yhats "`yhat2' `yhat3' `yhat4'"
local msg1 "square, cube and 4th power of "
}
* Demean yhats
if "`e(cmd)'" == "xtivreg2" {
qui foreach var of local yhats {
tempname `var'_m
by `ivar' `touse' : gen double ``var'_m'=sum(`var')/_N if `touse'
by `ivar' `touse' : replace ``var'_m'=``var'_m'[_N] if `touse' & _n<_N
by `ivar' `touse' : replace `var'=`var'-``var'_m'[_N] if `touse'
}
}
* Artificial regression test (default)
if "`cstat'"=="" {
capture _estimates hold `regest', restore
if "`endo'" != "" {
capture ivreg2 `depvar' `inexog' `yhats' (`endo'=`exexog') `wtexp' if `touse', /*
*/ `small' `xtopts' `robust' `clopt' `bwopt' `kernopt' `consopt'
}
else {
* No endogenous regressors, so ignore excluded exog (if any) as well
capture ivreg2 `depvar' `inexog' `yhats' `wtexp' if `touse', /*
*/ `small' `xtopts' `robust' `clopt' `bwopt' `kernopt' `consopt'
}
if "`e(cmd)'" == "xtivreg2" {
local df_aug = (`N_g'+e(df_m)-(`polynomial'-1))
}
else {
local df_aug = (e(df_m)-(`polynomial'-1))
}
if `df_aug' < `df_m' {
di as err "Error - collinearities in augmented regression equation."
di as err "If using higher order polynomials, try reducing the order."
error 103
}
else {
capture test `yhats'
return scalar df=r(df)
return scalar p=r(p)
if "`small'" == "" {
return scalar chi2=r(chi2)
}
else {
return scalar F=r(F)
return scalar df_r=r(df_r)
}
}
capture _estimates unhold `regest'
}
* Cstat test (GMM distance)
if "`cstat'" != "" {
capture _estimates hold `regest', restore
* If endo is empty, so is exexog and it's just an LM test
qui ivreg2 `depvar' `inexog' (`endo'=`exexog' `yhats') `wtexp' if `touse', /*
*/ orthog(`yhats') `small' `xtopts' `robust' `clopt' `bwopt' `kernopt' `consopt'
if e(cstat) > 0 & e(cstat) < . {
tempname cstat
scalar `cstat'=e(cstat)
return scalar df=e(cstatdf)
return scalar p=e(cstatp)
if "`small'" == "" {
return scalar chi2=`cstat'
}
else {
return scalar df_r=e(Fdf2)-return(df)
return scalar F=`cstat'/return(df)*return(df_r)/e(N)
}
}
capture _estimates unhold `regest'
}
di in g "`msg0'"
di in g "Test uses `msg1'`msg2'"
di in g "Ho: E(y|X) is linear in X"
if return(chi2)~=. | return(F)~=. {
if "`small'"=="" {
di in g "`msg3' test statistic: " /*
*/ _col(35) in g "Chi-sq(" return(df) ") = " /*
*/ in y %5.2f return(chi2) /*
*/ _col(55) in g "P-value = " /*
*/ in y %5.4f return(p)
}
else {
di in g "`msg3' test statistic: " /*
*/ _col(35) in g "F(" return(df) "," return(df_r) ") = " /*
*/ in y %5.2f return(F) /*
*/ _col(55) in g "P-value = " /*
*/ in y %5.4f return(p)
}
if "`msg3h'`msg3c'`msg3a'" ~= "" {
di in g "Test is `msg3h'`msg3c'`msg3a'robust"
}
}
else {
di as err "Error in estimating augmented equation - statistic not reported"
}
end