Stata 15 help for heckpoisson

[R] heckpoisson -- Poisson regression with sample selection

Syntax

heckpoisson depvar indepvars [if] [in] [weight], select([depvar_s =] indepvars_s [, noconstant offset(varname_os)]) [options]

options Description ------------------------------------------------------------------------- Model * select() specify selection equation: dependent and independent variables; whether to have constant term and offset variable noconstant suppress constant term exposure(varname_e) include ln(varname_e) in model with coefficient constrained to 1 offset(varname_o) include varname_o in model with coefficient constrained to 1 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) irr report incidence-rate ratios 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

Integration intpoints(#) set the number of integration (quadrature) points; default is intpoints(25)

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics ------------------------------------------------------------------------- * select() is required. The full specification is select([depvar_s =] indepvars_s [, noconstant offset(varname_os)]). indepvars and indepvars_s may contain factor variables; see fvvarlist. indepvars and indepvars_s 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() and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [R] heckpoisson postestimation for features available after estimation.

Menu

Statistics > Sample-selection models > Poisson model with sample selection

Description

heckpoisson fits a Poisson regression model with endogenous sample selection. This is sometimes called nonignorability of selection, missing not at random, or selection bias. Unlike the standard Poisson model, there is no assumption of equidispersion.

Options

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

select([depvar_s =] indepvars_s [, noconstant offset(varname_os)]) specifies the variables and options for the selection equation. It is an integral part of specifying a sample-selection model and is required.

If depvar_s is specified, it should be coded as 0 or 1, with 0 indicating an observation not selected and 1 indicating a selected observation. If depvar_s is not specified, then observations for which depvar is not missing are assumed selected and those for which depvar is missing are assumed not selected.

noconstant suppresses the selection constant term (intercept).

offset(varname_os) specifies that selection offset varname_os be included in the model with the coefficient constrained to be 1.

noconstant, exposure(varname_e), offset(varname_o), 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(#); see [R] estimation options.

irr reports estimated coefficients transformed to incidence-rate ratios, that is, e^{beta_i} rather than beta_i. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated or stored. irr may be specified at estimation or when replaying previously estimated results.

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.

+-------------+ ----+ Integration +------------------------------------------------------

intpoints(#) specifies the number of integration points to use for quadrature. The default is intpoints(25), which means that 25 quadrature points are used. The maximum number of allowed integration points is 128.

The more integration points, the more accurate the approximation to the log likelihood. However, computation time increases with the number of quadrature points and is roughly proportional to the number of points used.

+--------------+ ----+ 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.

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

coeflegend; see [R] estimation options.

Examples

Setup . webuse patent

Fit a poisson model with endogenous sample selection . heckpoisson npatents expenditure i.tech, select(applied = expenditure size i.tech)

Replay results, but display legend of coefficients rather than the statistics for the coefficients . heckpoisson, coeflegend

Stored results

heckpoisson stores the following in e():

Scalars e(N) number of observations e(N_selected) number of selected observations e(N_nonselected) number of nonselected 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_aux) number of auxiliary parameters e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log likelihood e(N_clust) number of clusters e(chi2) chi-squared e(chi2_c) chi-squared for comparison, rho=0 test e(n_quad) number of quadrature points e(p) p-value for model test e(p_c) p-value for comparison test 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) heckpoisson 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(offset1) offset for regression equation e(offset2) offset for selection equation e(chi2type) Wald; type of model chi-squared test e(chi2_ct) Wald; type of comparison chi-squared test 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(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


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