+-------+ ----+ Title +------------------------------------------------------------
tobithetm: Tobit Multiplicative Heteroscedasticity Regression
+--------+ ----+ Syntax +-----------------------------------------------------------
tobithetm depvar [indepvars] [if] [in] [weight] , het(varlist [, offset(varname)]) [options]
options Description ------------------------------------------------------------------------- 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. INCLUDE help fvvarlist2 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 +------------------------------------------------------
tobithetm fits a maximum-likelihood for Tobit 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] tobit.
constraints(constraints), collinear; see [R] estimation options.
+-----------+ ----+ SE/Robust +--------------------------------------------------------
INCLUDE help vce_asymptall
+-----------+ ----+ 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 tobithetm but is not shown in the dialog box:
coeflegend; see [R] estimation options.
+---------------+ ----+ Saved Results +----------------------------------------------------
tobithetm 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) tobithetm 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 tobit 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 tobithetm.dta, clear
db tobithetm
tobithetm 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 - Eg > ypt 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
+--------------------+ ----+ tobithetm Citation +-----------------------------------------------
Shehata, Emad Abd Elmessih (2011) tobithetm: "Stata Module to Estimate Tobit Multiplicative Heteroscedasticity Regression"
http://ideas.repec.org/c/boc/bocode/s457323.html
http://econpapers.repec.org/software/bocbocode/s457323.htm