Stata 15 help for churdle

[R] churdle -- Cragg hurdle regression


Basic syntax

churdle linear depvar, select(varlist_s) {ll(...) | ul(...)}

churdle exponential depvar, select(varlist_s) ll(...)

Full syntax for churdle linear

churdle linear depvar [indepvars] [if] [in] [weight], select(varlist_s[, noconstant het(varlist_o)]) {ll(#|varname) | ul(#|varname)} [options]

Full syntax for churdle exponential

churdle exponential depvar [indepvars] [if] [in] [weight], select(varlist_s[, noconstant het(varlist_o)]) ll(#|varname) [options]

options Description ------------------------------------------------------------------------- Model * select() specify independent variables and options for selection model + ll(#|varname) lower truncation limit + ul(#|varname) upper truncation limit noconstant suppress constant term constraints(constraints) apply specified linear constraints het(varlist) specify variables to model the variance

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

Reporting level(#) set confidence level; default is level(95) 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 ------------------------------------------------------------------------- * select() is required. The full specification is select(varlist_s[, noconstant het(varlist_o)]) noconstant specifies that the constant be excluded from the selection model. het(varlist_o) specifies the variables in the error-variance function of the selection model. + You must specify at least one of ul(#|varname) or ll(#|varname) for the linear model and must specify ll(#|varname) for the exponential model. indepvars, varlist_s, and varlist_o may contain factor variables; see fvvarlist. bootstrap, by, fp, 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] churdle postestimation for features available after estimation.


Statistics > Linear models and related > Hurdle regression


churdle fits a linear or exponential hurdle model for a bounded dependent variable. The hurdle model combines a selection model that determines the boundary points of the dependent variable with an outcome model that determines its nonbounded values. Separate independent covariates are permitted for each model.


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

select(varlist_s[, noconstant het(varlist_o)]) specifies the variables and options for the selection model. select() is required.

ll(#|varname) and ul(#|varname) indicate the lower and upper limits, respectively, for the dependent variable. You must specify one or both for the linear model and must specify a lower limit for the exponential model. Observations with depvar <= ll() have a lower bound; observations with depvar >= ul() have an upper bound; and the remaining observations are in the continuous region.

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

het(varlist) specifies the variables in the error-variance function of the outcome model.

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

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim), 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(#), 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.

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

coeflegend; see [R] estimation options.


--------------------------------------------------------------------------- Setup . webuse fitness

Cragg hurdle linear regression . churdle linear hours age i.smoke distance i.single, select(commute whours age) ll(0)

Average marginal effect of age . margins, dydx(age)

Cragg hurdle linear regression with a model for the variance . churdle linear hours age i.smoke distance i.single, select(commute whours age, het(age single)) ll(0)

Cragg hurdle exponential regression . churdle exponential hours age i.smoke distance i.single, select(commute whours age) ll(0) nolog

Average marginal effect of age . margins, dydx(age)

--------------------------------------------------------------------------- Setup . webuse nhanes2f, clear . svyset psuid [pweight=finalwgt], strata(stratid)

Cragg hurdle linear regression with survey data . svy: churdle linear finalwgt i.female copper, ll(2000) select(highbp agegrp)


Stored results

churdle stores the following in e():

Scalars e(N) number of observations e(k_eq_model) number of equations in overall model test e(df_m) model degrees of freedom e(r2_p) pseudo-R-squared e(chi2) chi-squared e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(N_clust) number of clusters e(p) p-value for model test e(rank) rank of e(v) e(converged) 1 if converged, 0 otherwise

Macros e(cmd) churdle e(cmdline) command as typed e(depvar) name of dependent variable e(estimator) linear or exponential e(model) Linear or Exponential e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(chi2type) Wald or LR; type of model 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(technique) maximization technique e(properties) b V e(predict) program used to implement predict 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 e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

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