help: logithetm dialog:

Title

logithetm: Logit Multiplicative Heteroscedasticity Regression

Syntax

logithetm depvar [indepvars] [if] [in] [weight] ,
het(varlist [, offset(varname)]) [options]

Model
* het(varlist[...]) independent variables to model the variance and possible offset variable

noconstant suppress constant term

offset(varname) include varname in model with coefficient constrained to 1

asis retain perfect predictor variables

constraints(constraints) apply specified linear constraints

collinear keep collinear variables

SE/Robust
vce(vcetype) vcetype may be oim,
robust, cluster clustvar, opg, bootstrap,
or jackknife

Reporting
level(#) set confidence level; default is level(95)

noskip perform likelihood-ratio test

nolrtest perform Wald test on variance

nocnsreport do not display constraints

display_options control spacing
and display of omitted variables and base and empty cells

Maximization
maximize_options control the maximization process; seldom used

+ coeflegend display coefficients' legend instead of coefficient table

* het() is required. The full specification is

het(varlist [, offset(varname)])

+ coeflegend does not appear in the dialog box.

depvar, indepvars, and varlist may contain time-series
operators; see tsvarlist.

bootstrap, by, jackknife, rolling,
statsby, and svy are allowed; see prefix.

Weights are not allowed with the bootstrap prefix.

vce(), noskip, and weights are not allowed with the svy
prefix.

fweights, iweights, and pweights are allowed; see weight.

Description

logithetm fits a maximum-likelihood for Logit Multiplicative Heteroscedasticity Regression.

Options

Model

het(varlist [, offset(varname)]) specifies the
independent variables and the offset variable, if there is one, in the
variance function. het() is required.

noconstant, offset(varname); see
[R] estimation options.

asis forces the retention of perfect predictor variables and their
associated perfectly predicted observations and may produce instabilities in
maximization; see [R] logit.

constraints(constraints), collinear; see
[R] estimation options.

SE/Robust

Reporting

level(#); see
[R] estimation options.

noskip requests fitting of the constant-only model and calculation of
the corresponding likelihood-ratio chi-squared statistic for testing
significance of the full model. By default, a Wald chi-squared statistic is
computed for testing the significance of the full model.

nolrtest specifies that a Wald test of whether lnsigma2 = 0 be
performed instead of the LR test.

nocnsreport; see
[R] estimation options.

display_options:
noomitted,
vsquish,
noemptycells,
baselevels,
allbaselevels;
see [R] estimation options.

Maximization

maximize_options: difficult, technique(algorithm_spec),
iterate(#), [no]log, trace,
gradient, showstep,
hessian, showtolerance,
tolerance(#), ltolerance(#),
nrtolerance(#), nonrtolerance,
from(init_specs); see [R] maximize. These options are seldom
used.

Setting the optimization type to technique(bhhh) resets the default
vcetype to vce(opg).

The following option is available with logithetm but is not shown in the
dialog box:

coeflegend; see
[R] estimation options.

Saved Results

logithetm saves the following in e():

Scalars

e(N) number of observations

e(N_f) number of zero outcomes

e(N_s) number of nonzero outcomes

e(k) number of parameters

e(k_eq) number of equations

e(k_eq_model) number of equations in model Wald test

e(k_dv) number of dependent variables

e(k_autoCns) number of base, empty, and omitted constraints

e(df_m) model degrees of freedom

e(ll) log likelihood

e(chi2) chi-squared

e(chi2_c) chi-squared for heteroskedasticity LR test

e(p) significance

e(rank) rank of e(V)

e(ic) number of iterations

e(rc) return code

e(converged) 1 if converged, 0 otherwise

Macros

e(cmd) logithetm

e(cmdline) command as typed

e(depvar) name of dependent variable

e(wtype) weight type

e(wexp) weight expression

e(title) title in estimation output

e(clustvar) name of cluster variable

e(offset1) offset for Logit equation

e(offset2) offset for variance equation

e(chi2type) Wald or LR; type of model chi-squared
test

e(chi2_ct) Wald or LR; type of model chi-squared
test corresponding to e(chi2_c)

e(vce) vcetype specified in vce()

e(vcetype) title used to label Std. Err.

e(opt) type of optimization

e(which) max or min; whether optimizer is to perform
maximization or minimization

e(method) requested estimation method

e(ml_method) type of ml method

e(user) name of likelihood-evaluator program

e(technique) maximization technique

e(singularHmethod) m-marquardt or hybrid; method used
when Hessian is singular

e(crittype) optimization criterion

e(properties) b V

e(predict) program used to implement predict

e(asbalanced) factor variables fvset as asbalanced

e(asobserved) factor variables fvset as asobserved

Matrices

e(b) coefficient vector

e(Cns) constraints matrix

e(ilog) iteration log (up to 20 iterations)

e(gradient) gradient vector

e(V) variance-covariance matrix of the estimators

e(V_modelbased) model-based variance

Functions

e(sample) marks estimation sample

Examples

clear all

sysuse logithetm.dta, clear

db logithetm

logithetm y x1 , het(x1)

References

Greene, William (1993)
"Econometric Analysis",
2nd ed., Macmillan Publishing Company Inc., New York, USA..

Author

Emad Abd Elmessih Shehata
Assistant Professor
Agricultural Research Center - Agricultural Economics Research Institute - Egypt
Email: emadstat@hotmail.com
WebPage: http://emadstat.110mb.com/stata.htm
WebPage at IDEAS: http://ideas.repec.org/f/psh494.html
WebPage at EconPapers: http://econpapers.repec.org/RAS/psh494.htm

logithetm Citation

Shehata, Emad Abd Elmessih (2011)

logithetm: "Stata Module to Estimate Logit Multiplicative Heteroscedasticity Regression"

http://ideas.repec.org/c/boc/bocode/s457324.html

http://econpapers.repec.org/software/bocbocode/s457324.htm