help tpm postestimationalso see: tpm

tpm postestimation -- Postestimation tools for tpm

Description

The following postestimation commands are available for tpm:

command description

estat AIC, BIC, VCE, and estimation sample summary

predict predictions

Syntax for predict

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

predict [type] {stub*|newvar1 ... newvarq}
[if] [in] , scores

options

Main
normal uses normal theory retransformation to obtain fitted values

duan uses Duan's smearing retransformation to obtain fitted values

scores calculate first derivative of the log likelihood with respect to xb

Options
nooffset ignore any offset() or exposure() variable

Description

predict returns E(depvar|). In particular, the combined prediction
is computed as the product of the probability of a positive outcome (first part) and
the expected value of Y | Y>0 (second part). This statistic is available both in and
out of sample; type predict ... if e(sample) ... if wanted only for the
estimation sample.

Options for predict

Main

normal uses normal theory retransformation to obtain fitted values. Either normal or duan must be specified when a linear regression of the log of the second part outcome is estimated.

duan uses Duan's smearing retransformation to obtain fitted values. Either normal or duan must be specified when a linear regression of the log of the second part outcome is estimated.

scores create a score variable for each equation (part) in the model.
Since the score for the second part of the model make sense only with respect
to the estimation subsample (where Y>0), the calculation is automatically restricted to the
estimation subsample.

Options

nooffset may be combined with most statistics and specifies that
the calculation should be made, ignoring any offset or exposure variable
specified when the model was fit.

If neither the offset(varname_o) option nor the
exposure(varname_e) option was specified when the model was fit,
specifying nooffset does nothing.

Remarks

Retransformation after OLS regression of ln(depvar) is needed to obtain
consistent predictions of depvar. tpm implements this using normal theory
and smearing retransformations but both assume that the errors in the regression are
homoscedastic. Retransformation in the case of heteroscedastic errors is conceptually
complex and we have not implemented it in tpm. We suggest the gamma GLM
with log link as an alternative to a regression of ln(depvar).

Examples

Setup

. webuse womenwk, clear

. replace wage = 0 if wage==.

Two part model with logit and glm with Gaussian family and identity link

. tpm wage educ age married children, first(logit) second(glm)

. predict wagehat1

Two part model with probit and glm with gamma family and log link

. tpm wage educ age married children, f(probit) s(glm, fam(gamma) link(log))

. margins, dydx(*)

Two part model with probit and linear regression

. tpm wage educ age married children, f(probit) s(regress)

. margins, dydx(*)

Two part model with probit and linear regression of log(depvar>0)

. tpm wage educ age married children, f(probit) s(regress, log)

. margins, dydx(*)

. margins, predict(duan) dydx(*)