Stata 15 help for hetregress

[R] hetregress -- Heteroskedastic linear regression

Syntax

Maximum likelihood estimation

hetregress depvar [indepvars] [if] [in] [weight] [, ml_options]

Two-step GLS estimation

hetregress depvar [indepvars] [if] [in], twostep het(varlist) [ts_options]

ml_options Description ------------------------------------------------------------------------- Model mle use maximum likelihood estimator; the default het(varlist) independent variables to model the variance noconstant suppress constant term 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) lrmodel perform the LR model test instead of the default Wald model test waldhet perform Wald test on variance instead of LR test nocnsreport do not display constraints display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics -------------------------------------------------------------------------

ts_options Description ------------------------------------------------------------------------- Model * twostep use two-step GLS estimator; default is maximum likelihood * het(varlist) independent variables to model the variance noconstant suppress constant term

SE vce(vcetype) vcetype may be conventional, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

coeflegend display legend instead of statistics ------------------------------------------------------------------------- * twostep and het() are required.

indepvars and varlist may contain factor variables; see fvvarlist. depvar, indepvars, and varlist may contain time-series operators; see tsvarlist. bayes, bootstrap, by, fp, jackknife, rolling, statsby, and svy are allowed; see prefix. For more details, see [BAYES] bayes: hetregress. Weights are not allowed with the bootstrap prefix. aweights are not allowed with the jackknife prefix. vce(), lrmodel, twostep, and weights are not allowed with the svy prefix. aweights, fweights, iweights, and pweights are allowed with maximum likelihood estimation; see weight. coeflegend does not appear in the dialog box. See [R] hetregress postestimation for features available after estimation.

Menu

Statistics > Linear models and related > Heteroskedastic linear regression

Description

hetregress fits a multiplicative heteroskedastic linear regression by modeling the variance as an exponential function of the specified variables using either maximum likelihood (ML; the default) or Harvey's two-step generalized least-squares (GLS) method.

hetreg is a synonym for hetregress.

Options for maximum likelihood estimation

+-------+ ----+ Model +------------------------------------------------------------

mle requests that the maximum likelihood estimator be used. This is the default.

het(varlist) specifies the independent variables in the variance function. When the het() option is not specified, homoskedasticity is assumed and the waldhet option is not allowed.

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

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim, opg), that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

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

waldhet specified that the Wald test of whether lnsigma2 = 0 be performed instead of the LR test.

nocnsreport; see [R] estimation options.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and 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 hetregress but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Options for two-step GLS estimation

+-------+ ----+ Model +------------------------------------------------------------

twostep specifies that the model be fit using Harvey's two-step GLS estimator. This option requires that the independent variables be specified in the het() option to model the variance.

het(varlist) specifies the independent variables in the variance function.

noconstant; see [R] estimation options.

+----+ ----+ SE +---------------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (conventional) and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

vce(conventional), the default, uses the two-step variance estimator derived by Heckman.

+-----------+ ----+ Reporting +--------------------------------------------------------

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

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

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

coeflegend; see [R] estimation options.

Examples

Setup . sysuse auto

Fit multiplicative heteroskedastic regression model and use length to model the variance . hetregress price length i.foreign, het(length)

Perform LR test instead of Wald test for the mean function . hetregress price length i.foreign, het(length) lrmodel

Fit heteroskedastic regression model using Harvey's two-step GLS method . hetregress price length i.foreign, het(length) twostep

Stored results

hetregress (ML) stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log likelihood, full model e(ll_0) log likelihood, constant-only model e(ll_c) log likelihood, comparison model e(N_clust) number of clusters e(chi2) chi-squared for mean model test e(chi2_c) chi-squared for heteroskedasticity test e(p_c) p-value for heteroskedasticity test e(df_m_c) degrees of freedom for heteroskedasticity test e(p) p-value for the mean model test e(rank) rank of e(V) e(rank0) rank of e(V) for constant-only model e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) hetregress 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(title2) secondary title in estimation output e(clustvar) name of cluster variable e(chi2type) Wald or LR; type of model chi-squared test e(chi2_ct) Wald or LR; type of heteroskedastic 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) ml e(ml_method) type of ml method e(user) name of likelihood-evaluator program e(technique) maximization technique e(properties) b V e(predict) program used to implement predict e(marginsok) predictions allowed by margins e(marginsnotok) predictions disallowed by margins 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

hetregress (two-step GLS) stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(df_m) model degrees of freedom e(chi2) chi-squared for mean model test e(chi2_c) chi-squared for heteroskedasticity test e(p_c) p-value for heteroskedasticity test e(df_m_c) degrees of freedom for heteroskedasticity test e(p) p-value for the mean model test e(rank) rank of e(V)

Macros e(cmd) hetregress e(cmdline) command as typed e(depvar) name of dependent variable e(title) title in estimation output e(title2) secondary title in estimation output e(chi2type) Wald; type of model chi-squared test e(chi2_ct) Wald; type of heteroskedastic chi-squared test corresponding to e(chi2_c) e(vce) vcetype specified in vce() e(method) twostep e(properties) b V e(predict) program used to implement predict e(marginsok) predictions allowed by margins e(marginsnotok) predictions disallowed by margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(V) variance-covariance matrix of the estimators

Functions e(sample) marks estimation sample


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