{smcl}
{hline}
help for {hi:hnblogit} {right:(Joseph Hilbe)}
{hline}

{title:Negative binomial-logit hurdle regression}

{p 8 13 2}{cmd:hnblogit}{space 2}{it:depvar} [{it:varlist}] 
	[{cmd:if} {it:exp}] [{cmd:in} {it:range}]  [, {cmdab:off:set(}{it:varname}{cmd:)}
        {cmdab:exp:osure(}{it:varname}{cmd:)} {cmdab:cl:uster(}{it:varname}{cmd:)}
        {cmdab:l:evel(}{it:#}{cmd:)} {cmdab:from:(}{it:asis}{cmd:)} {cmdab:ir:r} {cmdab:rob:ust}
        {cmd:nolog} {it:maximize_options} {it:survey_options}]



{p 4 4 2}
{cmd:aweight}s, {cmd:fweight}s, {cmd:iweight}s, and {cmd:pweight}s are
allowed; see help {help weights}.

{p 4 4 2}
{cmd:hnblogit} provides access to all {it:maximize} options; see help {help maximize}.

{p 4 4 2}
{cmd:hnblogit} provides access to all {it:survey} options; see help {help svy}.


{title:Description}

{p 4 4 2}{cmd:hnblogit} fits a negative binomial-logit maximum-likelihood hurdle model 
of {it:depvar} on {it:indepvars}, where {it:depvar} is a non-negative count variable.

{p 4 4 2}{cmd:hnblogit} acccepts all of the {it:help maximize} options, the {it:constraint()} 
option, and all survey options and capabilities documented in {cmd:[SVY]}; including 
multi-level surveys; poststratification; and BRR, jackknife, and linearization VCE estimators.

{p 4 4 2}This program uses {cmd:ml lf} method. 


{title:Options}

{dlgtab:Model}

{phang}
{opth offset(varname)} specifies a {it:varname} in model with coefficient constrained to 1.

{phang}
{opth exposure(varname)} specifies a {it:ln(varname)} in model with coefficient constrained to 1.

{phang}
{opth constraints(constraints)} apply specified linear constraints.



{dlgtab:SE/Robust}

{phang}
{opth cluster(varname)} 

{p 4 8 2}
{cmd:robust} specifies that the Huber/White/sandwich estimator of
variance is to be used in place of the traditional calculation.  {cmd:robust}
combined with {cmd:cluster}{cmd:(}{cmd:)} allows observations which are not
independent within cluster (although they must be independent between
clusters).  If you specify {cmd:pweight}s, {cmd:robust} is implied.

{phang}
{opth vce(options)} allowed. {cmd:vce}{cmd:(}{cmd:)} supports {it:robust}, {it:opg}, and {it:native}.
{cmd:vce} does not support options {it:bootstrap} or {it:jacknife}, However,  {cmd:hnblogit} does support  
the {cmd:bootstrap} and {cmd:jacknife} commands, so these modeling capabilities are allowed.  


{dlgtab:Reporting}
   
{p 4 8 2}{cmd:level(}{it:#}{cmd:)} specifies the confidence level, in percent,
for confidence intervals of the coefficients; see help {help level}.

{p 4 8 2}
{cmd:nolog} suppresses the iteration log.


{dlgtab:max options}

{phang}

{p 4 8 2}
{it:maximize_options}: technique(algorithm_spec), [no]log, trace, hessian, gradient, showstep, 
shownrtolerance, difficult, iterate(#), tolerance(#), ltolerance(#), gtolerance(#), nrtolerance(#), 
nonrtolerance, from(init_specs); see {help maximize}.  


{dlgtab:svy options}

{phang}
{it:survey_options} are all available. See help {help svy}




{title:Author and support}

{phang}
{cmd: Joseph Hilbe},
{cmd: Arizona State University}:
{cmd: jhilbe@aol.com}


{title:Remarks}

{pstd}
{cmd:hnblogit} is a user authored program. Support is by author. {cmd:hnblogit}
is one of a suite of hurdle programs created by the author. 2Oct2005

{pstd}
{cmd:hnblogit} requires a response with counts including a number of 0's. {cmd:irr} for binary model.


{title:Examples}

{phang}{cmd:. hnblogit accident co_70_74 co_75_79 op_75_79, nolog exposure(service)}

{phang}{cmd:. hnblogit accident co_70_74 co_75_79 op_75_79, nolog exposure(service) cluster(ship)}

{phang}{cmd:. bootstrap: hnblogit accident co_70_74 co_75_79 op_75_79, nolog exposure(service)}

