*! Version 1.0.0 09 August 2017

Title

nehurdle postestimation ---- Postestimation tools
for nehurdle

Description

The following postestimation commands are available after nehurdle:

Command Description

contrast contrasts and ANOVA-style joint tests of estimates

estat ic Akaike's and Schwarz's Bayesian information criteria
(AIC and BIC)

estat summarize summary statistics for the estimation sample

estat vce variance-covariance matrix of the estimators (VCE)

estat (svy) postestimation statistics for survey
data

estimates cataloging estimation results

lincom point estimates, standard errors, testing, and inference
for linear combinations of coefficients

(1) lrtest likelihood-ratio test

margins marginal means, predictive margins, marginal effects,
and average marginal effects

marginsplot graph the results from margins (profile plots,
interaction plots, etc.)

(1) nehtests Wald tests of joint significance of all the estimated
equations, and the overall joint significance test

nlcom point estimates, standard errors, testing, and inference
for nonlinear combinations of coefficients

predict predictions,
residuals, influence statistics, and other diagnostic measures

predictnl point estimates, standard errors, testing, and
inference for generalized predictions

pwcompare pairwise comparisons of estimates

suest seemingly unrelated estimation

test Wald tests of simple and composite linear hypotheses

testnl Wald tests of nonlinear hypotheses

(1) lrtest and nehtests are not appropriate with svy estimation results.

Syntax for predict

General syntax

predict [type] newvar [if] [in] [, statistic]

Syntax for scores

predict [type] {stub*|newvarlist}
[if] [in]
, scores

statistic

All estimators
ycen E(y), prediction of the censored mean; the default

ytrun E(y | y > 0), prediction of the truncated mean

psel Pr(y > 0), prediction of probability of being observed

xbval linear prediction for value equation

xbsig linear prediction for natural logarithm of the standard deviation
of the value equation

sigma prediction for standard deviation of the value equation

rescen residuals of prediction of the censored mean

restrun residuals of prediction of the truncated mean

resval residuals of linear prediction for value equation

ressel residuals of prediction of probability of being observed

Truncated Hurdle and Type II Tobit
xbsel linear prediction for selection equation

xbselsig linear prediction for natural logarithm of the standard
deviation of selection equation

selsigma prediction of the standard deviation of selection
equation

Type II Tobit
lambda prediction of coefficient on inverse mills ratio
(covariance of the errors across equations)

Options for predict

All estimators

ycen, the default, calculates the prediction of the censored mean. When
using this option with margins dydx(*) after an Exponential Tobit estimation
you will need to add the force option to avoid an error from Stata.

ytrun calculates the prediction of the truncated mean, i.e. prediction of y
given that y is positive (and observed). When using this option with
margins dydx(*) after an Exponential Tobit estimation you will need to
add the force option to avoid an error from Stata.

psel calculates the probability that y is positive (and observed).

xbval calculates the linear prediction for the value equation. You can also
think about this as the linear prediction of the uncensored (latent) variable (mean),
y*. Notice that if you specified the value equation to be exponential, with the
exponential option, the prediction will be of the latent natural logarithm of y.

xbsig calculates the linear prediction for natural logarithm of the
standard deviation of the value equation. For homoskedastic value equations,
this will be equal to the coefficient on /lnsigma. However, if you have
modeled heteroskedasticity in the value equation the prediction will vary
across observations.

sigma calculates the prediction for standard deviation of the value
equation. It is simply the exponential of the prediction you would get with
xbsig.

rescen calculates the residuals against the censored mean. That is y -
E(y), where E(y) is the censored mean you get with the
ycen option.

restrun calculates the residuals against the truncated mean. That is y -
E(y | y > 0), where E(y | y > 0) is the truncated mean you
get with the ytrun option.

resval calculates the residuals against the linear prediction for value
equation that you would get with xbval. Notice that if you have
specified the value equation to be exponential these residuals would be
limited to those observations with a positive value of y, since ln(y) is
indeterminate for those observations where y = 0.

ressel residuals of the prediction of probability of being observed. This
this is calculated as the difference between the dummy variable that identifies
whether y > 0, and the predicted Pr(y > 0) that you get with psel.

Truncated Hurdle and Type II Tobit

xbsel calculates the linear prediction for selection equation.

xbselsig calculates the linear prediction of the natural logarithm of the
standard deviation of selection equation. This option is only available if
you have modeled heteroskedasticity in the selection equation, for otherwise
the prediction would be 0 since the Probit estimator normalizes the standard
deviation to 1.

selsigma calculates the prediction of the standard deviation of the selection
equation. Like with xbselsig this is only available if you have modeled
heteroskedasticity in the selection equation, and for the same reasons. This
is calculated by taking the exponential of the prediction with the xbselsig
option.

Type II Tobit

lambda calculates the prediction of what some people call the coefficient
on inverse mills ratio. It is a prediction of the covariance of the errors
of both equations (selection and value). It is calculated by multiplying the
prediction of the standard deviation of the selection equation times the
prediction of the standard deviation of the value equation times the estimate
of the correlation of the errors of both equations.

Examples

Data Setup

. webuse womenwk, clear

. replace wage = 0 if missing(wage)

. global xvars i.married children educ age

Homoskedastic Tobit

. nehurdle wage $xvars, tobit nolog

. margins, dydx(*) // AMEs on censored mean

. margins, dydx(*) predict(ytrun) // AMEs on truncated mean

Heteroskedastic Exponential Truncated Hurdle

. nehurdle wage $xvars, expon het($xvars) nolog

. margins, dydx(*) // AMEs on censored mean

. margins, dydx(*) predict(sigma) // AMEs on the standard deviation of value equation

. predict rsel, rescen // Residuals against the censored prediction.

Heteroskedastic Exponential Type II Tobit

. nehurdle wage $xvars, heckman expon het($xvars) nolog

. margins, dydx(*) // AMEs on censored mean

. margins, predict(lambda) // Estimate of the coefficient on inverse-mills ratio