Stata 15 help for etpoisson

[TE] etpoisson -- Poisson regression with endogenous treatment effects


etpoisson depvar [indepvars] [if] [in] [weight], treat(depvar_t = indepvars_t [, noconstant offset(varname_o)]) [options]

options Description ------------------------------------------------------------------------- Model * treat() equation for treatment effects 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(#) use # Gauss-Hermite quadrature points; default is intpoints(24)

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics ------------------------------------------------------------------------- * treat() is required. The full specification is treat(depvar_t = indepvars_t [, noconstant offset(varname_o)]).

indepvars and indepvars_t may contain factor variables; see fvvarlist. depvar, depvar_t, indepvars, and indepvars_t 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. aweights are not allowed with the jackknife prefix. vce() and weights are not allowed with the svy prefix. fweights, aweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [TE] etpoisson postestimation for features available after estimation.


Statistics > Treatment effects > Endogenous treatment > Maximum likelihood estimator > Count outcomes


etpoisson estimates the parameters of a Poisson regression model in which one of the regressors is an endogenous binary treatment. Both the average treatment effect and the average treatment effect on the treated can be estimated with etpoisson.


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

treat(depvar_t = indepvars_t[, noconstant offset(varname_o)]) specifies the variables and options for the treatment equation. It is an integral part of specifying a treatment-effects model and is required.

The indicator of treatment, depvar_t, should be coded as 0 or 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, exp(b) rather than b. 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 integration by quadrature. The default is intpoints(24); the maximum is intpoints(128). Increasing this value improves the accuracy but also increases computation time. Computation time is roughly proportional to its value.

+--------------+ ----+ 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 etpoisson but is not shown in the dialog box:

coeflegend; see [R] estimation options.


Setup . webuse trip1

Fit a Poisson regression with endogenous treatment . etpoisson trips cbd ptn worker weekend, treat(owncar = cbd ptn worker realinc) vce(robust)

Estimate average treatment effect . margins r.owncar, vce(unconditional)

Estimate average treatment effect on the treated . margins, predict(cte) vce(unconditional) subpop(owncar)

Stored results

etpoisson 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_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) etpoisson 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 treatment 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(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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