help ztnbp

Title

ztnbp -- Zero-truncated NegBin-P regression

Syntax

ztnbp
depvar
[indepvars]
[if] [in]
[,
options]

where depvar has to be a strictly postive outcome.

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