**[R] churdle** -- Cragg hurdle regression

__Syntax__

Basic syntax

**churdle** __lin__**ear** *depvar***,** __sel__**ect(***varlist_s***)** {**ll(**...**)** | **ul(**...**)**}

**churdle** __exp__**onential** *depvar***,** __sel__**ect(***varlist_s***)** **ll(**...**)**

Full syntax for churdle linear

**churdle** __lin__**ear** *depvar* [*indepvars*] [*if*] [*in*] [*weight*]**,**
__sel__**ect(***varlist_s*[**,** __nocons__**tant** **het(***varlist_o***)**]**)** {**ll(***#*|*varname***)**
| **ul(***#*|*varname***)**} [*options*]

Full syntax for churdle exponential

**churdle** __exp__**onential** *depvar* [*indepvars*] [*if*] [*in*] [*weight*]**,**
__sel__**ect(***varlist_s*[**,** __nocons__**tant** **het(***varlist_o***)**]**)** **ll(***#*|*varname***)**
[*options*]

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

SE/Robust
**vce(***vcetype***)** *vcetype* may be **oim**, __r__**obust**, __cl__**uster** *clustvar*,
__boot__**strap**, or __jack__**knife**

Reporting
__l__**evel(***#***)** set confidence level; default is **level(95)**
__nocnsr__**eport** 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

__coefl__**egend** display legend instead of statistics
-------------------------------------------------------------------------
* **select()** is required. The full specification is
__sel__**ect(***varlist_s*[**,** __nocons__**tant** **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.
**fweight**s, **iweight**s, and **pweight**s are allowed; see weight.
**coeflegend** does not appear in the dialog box.
See **[R] churdle postestimation** for features available after estimation.

__Menu__

**Statistics > Linear models and related > Hurdle regression**

__Description__

**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.

__Options__

+-------+
----+ 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**, __nopv__**alues**, __noomit__**ted**, **vsquish**, __noempty__**cells**,
__base__**levels**, __allbase__**levels**, __nofvlab__**el**, **fvwrap(***#***)**, **fvwrapon(***style***)**,
**cformat(***%fmt***)**, **pformat(%***fmt***)**, **sformat(%***fmt***)**, and **nolstretch**; see **[R]**
**estimation options**.

+--------------+
----+ Maximization +-----------------------------------------------------

*maximize_options*: __dif__**ficult**, __tech__**nique(***algorithm_spec***)**, __iter__**ate(***#***)**,
[__no__]__lo__**g**, __tr__**ace**, __grad__**ient**, **showstep**, __hess__**ian**, __showtol__**erance**,
__tol__**erance(***#***)**, __ltol__**erance(***#***)**, __nrtol__**erance(***#***)**, __nonrtol__**erance**, 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**.

__Examples__

---------------------------------------------------------------------------
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