Title
ztnbp -- Zero-truncated NegBin-P regression
Syntax
ztnbp depvar [indepvars] [if] [in] [, options]
where depvar has to be a strictly postive outcome.
options Description ------------------------------------------------------------------------- noconstant suppress constant term vce(vcetype) vcetype may be oim, robust, cluster clustvar, or opg maximize_options control the maximization process; see [R] maximize ------------------------------------------------------------------------- bootstrap and jackknife are allowed; see prefix.
Description
ztnbp fits a zero-truncated Negbin-P model. Setting P=1 or P=2 gives the ztnb-1 (dispersion(constant)) or ztnb-2 (dispersion(mean)) model (see tnbreg). Otherwise ztnbp generalizes these models in the sense that you get an estimate for P.
This program uses ml lf method.
Options
noconstant suppresses the constant term (intercept) in the model.
vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory, that are robust to some kinds of misspecification, and that allow for intragroup correlation; see [R] vce_option.
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.
difficult is the default.
Author
Helmut Farbmacher Munich Center for the Economics of Aging (MEA) Max Planck Society, Germany farbmacher@mea.mpisoc.mpg.de
Reference
Farbmacher, H. 2012: Extensions of hurdle models for overdispersed count data, Health Economics, forthcoming.
Help: [R] tnbreg, [R] ztpnm, [R] ztpflex