Stata 15 help for heckman_postestimation

[R] heckman postestimation -- Postestimation tools for heckman

Postestimation commands

The following postestimation commands are available after heckman:

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 + hausman Hausman's specification test lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients + lrtest likelihood-ratio test; not available with two-step estimator margins marginal means, predictive margins, marginal effects, and average marginal effects marginsplot graph the results from margins (profile plots, interaction plots, etc.) 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 ------------------------------------------------------------------------- * estat ic and suest are not appropriate after heckman, twostep. + hausman and lrtest are not appropriate with svy estimation results.

Syntax for predict

After ML or twostep

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

After ML

predict [type] {stub*|newvar_reg newvar_sel newvar_athrho newvar_lnsigma} [if] [in] , scores

statistic Description ------------------------------------------------------------------------- Main xb linear prediction; the default stdp standard error of the prediction stdf standard error of the forecast xbsel linear prediction for selection equation stdpsel standard error of the linear prediction for selection equation pr(a,b) Pr(y | a < y < b) e(a,b) E(y | a < y < b) ystar(a,b) E(y*), y* = max{a,min(y,b)} ycond E(y | y observed) yexpected E(y*), y taken to be 0 where unobserved nshazard or mills nonselection hazard (also called inverse of Mills's ratio) psel Pr(y observed) ------------------------------------------------------------------------- These statistics are available both in and out of sample; type predict ... if e(sample) ... if wanted only for the estimation sample. stdf is not allowed with svy estimation results.

where a and b may be numbers or variables; a missing (a > .) means minus infinity, and b missing (b > .) means plus infinity; see missing.

Menu for predict

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as linear predictions, standard errors, probabilities, expected values, and nonselection hazards.

Options for predict

+------+ ----+ Main +-------------------------------------------------------------

xb, the default, calculates the linear prediction.

stdp calculates the standard error of the prediction, which can be thought of as the standard error of the predicted expected value or mean for the observation's covariate pattern. The standard error of the prediction is also referred to as the standard error of the fitted value.

stdf calculates the standard error of the forecast, which is the standard error of the point prediction for 1 observation. It is commonly referred to as the standard error of the future or forecast value. By construction, the standard errors produced by stdf are always larger than those produced by stdp; see Methods and formulas in [R] regress postestimation.

xbsel calculates the linear prediction for the selection equation.

stdpsel calculates the standard error of the linear prediction for the selection equation.

pr(a,b) calculates Pr(a < xb + u < b), the probability that y|x would be observed in the interval (a,b).

a and b may be specified as numbers or variable names; lb and ub are variable names; pr(20,30) calculates Pr(20 < xb + u < 30); pr(lb,ub) calculates Pr(lb < xb + u < ub); and pr(20,ub) calculates Pr(20 < xb + u < ub).

a missing (a > .) means minus infinity; pr(.,30) calculates Pr(xb + u < 30); pr(lb,30) calculates Pr(xb + u < 30) in observations for which lb > . and calculates Pr(lb < xb + u < 30) elsewhere.

b missing (b > .) means plus infinity; pr(20,.) calculates Pr(xb + u > 20); pr(20,ub) calculates Pr(xb + u > 20) in observations for which ub > . and calculates Pr(20 < xb + u < ub) elsewhere.

e(a,b) calculates E(xb + u | a < xb + u < b), the expected value of y|x conditional on y|x being in the interval (a,b), meaning that y|x is truncated. a and b are specified as they are for pr().

ystar(a,b) calculates E(y*), where y* = a if xb + u < a, y* = b if xb + u > b, and y* = xb + u otherwise, meaning that y* is not selected. a and b are specified as they are for pr().

ycond calculates the expected value of the dependent variable conditional on the dependent variable being observed, that is, selected.

yexpected calculates the expected value of the dependent variable (y*), where that value is taken to be 0 when it is expected to be unobserved.

The assumption of 0 is valid for many cases where nonselection implies nonparticipation (for example, unobserved wage levels, insurance claims from those who are uninsured) but may be inappropriate for some problems (for example, unobserved disease incidence).

nshazard and mills are synonyms; both calculate the nonselection hazard -- what Heckman (1979) referred to as the inverse of the Mills ratio -- from the selection equation.

psel calculates the probability of selection (or being observed).

nooffset is relevant when you specify offset(varname) for heckman. It modifies the calculations made by predict so that they ignore the offset variable; the linear prediction is treated as xb rather than as xb + offset.

scores, not available with twostep, calculates equation-level score variables.

The first new variable will contain the derivative of the log likelihood with respect to the regression equation.

The second new variable will contain the derivative of the log likelihood with respect to the selection equation.

The third new variable will contain the derivative of the log likelihood with respect to the third equation (athrho).

The fourth new variable will contain the derivative of the log likelihood with respect to the fourth equation (lnsigma).

Syntax for margins

margins [marginlist] [, options]

margins [marginlist] , predict(statistic ...) [predict(statistic ...) ...] [options]

statistic Description ------------------------------------------------------------------------- xb linear prediction; the default xbsel linear prediction for selection equation pr(a,b) Pr(y | a < y < b) e(a,b) E(y | a < y < b) ystar(a,b) E(y*), y* = max{a,min(y,b)} * ycond E(y | y observed) * yexpected E(y*), y taken to be 0 where unobserved nshazard or mills nonselection hazard (also called inverse of Mills's ratio) psel Pr(y observed) stdp not allowed with margins stdf not allowed with margins stdpsel not allowed with margins ------------------------------------------------------------------------- * ycond and yexpected are not allowed with margins after heckman, twostep.

Statistics not allowed with margins are functions of stochastic quantities other than e(b).

For the full syntax, see [R] margins.

Menu for margins

Statistics > Postestimation

Description for margins

margins estimates margins of response for linear predictions, probabilities, expected values, and nonselection hazards.

Examples

Setup . webuse womenwk . heckman wage educ age, select(married children educ age)

Predicted wage conditional on it being observed . predict ycond, ycond

Probability of wage being observed . predict probseen, psel

Reference

Heckman, J. 1979. Sample selection bias as a specification error. Econometrica 47: 153-161.


© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index