Stata 15 help for churdle_postestimation

[R] churdle postestimation -- Postestimation tools for churdle

Postestimation commands

The following standard postestimation commands are available after churdle:

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 * forecast dynamic forecasts and simulations * hausman Hausman's specification test lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients * 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.) 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 ------------------------------------------------------------------------- * forecast, hausman, and lrtest are not appropriate with svy estimation results.

Syntax for predict

predict [type] newvar [if] [in] [, statistic equation(eqno)]

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

statistic Description ------------------------------------------------------------------------- Main ystar conditional expectation of depvar; the default residuals residuals ystar(a,b) E(y*), y*=max{a, min(y,b)} xb linear prediction stdp standard error of the linear prediction pr(a,b) Pr(a < y < b) e(a,b) E(y | a < y < b) ------------------------------------------------------------------------- These statistics are available both in and out of sample; type predict ... if e(sample) ... if wanted only for the estimation sample.

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

Menu for predict

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as conditional expectation of depvar, residuals, linear predictions, standard errors, and probabilities.

Options for predict

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

ystar, the default, calculates the conditional expectation of the dependent variable.

residuals calculates the residuals.

ystar(a,b) calculates E(y*). a and b are specified as they are for pr(). If a and b are equal to the lower and upper bounds specified in churdle, then E(y*) is equivalent to the predicted value of the dependent variable ystar.

xb calculates the linear prediction.

stdp calculates the standard error of the linear prediction.

pr(a,b) calculates Pr(a < y < 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 < y<30); pr(lb,ub) calculates Pr(lb < y < ub); and pr(20, ub) calculates Pr(20 < y < ub).

a missing (a > .) means ll; pr(.,30) calculates Pr(ll < y < 30); pr(lb,30) calculates Pr(ll < y < 30) in observations for which lb > . and calculates Pr(lb < y < 30) elsewhere.

b missing (b > .) means plus infinity; pr(20,.) calculates Pr(infinity > y > 20); pr(20,ub) calculates Pr(infinity > y > 20) in observations for which ub > . and calculates Pr(ub > y > 20) elsewhere. For churdle linear, ul is infinity.

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

equation(eqno) specifies the equation for which predictions are to be made for the xb and stdp options. equation() should contain either one equation name or one of #1, #2, ... with #1 meaning the first equation, #2 meaning the second equation, etc.

If you do not specify equation(), the results are the same as if you specified equation(# 1).

scores calculates the equation-level score variables. If you specify one new variable, the scores for the latent-variable equation are computed. If you specify a variable list, the scores for each equation are calculated. The following scores may be obtained:

the first new variable will contain partial ln L/partial(x beta),

the second new variable will contain partial ln L/partial(z gamma_{ll}),

the third new variable will contain partial ln L/partial(z gamma_{ul}),

the fourth new variable will contain partial ln L/partial(sigma),

the fifth new variable will contain partial ln L/partial(sigma_{ll}), and

the sixth new variable will contain partial ln L/partial(sigma_{ul}).

Syntax for margins

margins [marginlist] [, options]

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

statistic Description ------------------------------------------------------------------------- Main ystar conditional expectation of depvar; the default ystar(a,b) E(y*), y*=max{a, min(y,b)}; for churdle exponential b is . xb linear prediction pr(a,b) Pr(a < y < b); for churdle exponential b is . e(a,b) E(y | a < y < b); for churdle exponential b is . residuals not allowed with margins stdp not allowed with margins -------------------------------------------------------------------------

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 conditional expectations, linear predictions, and probabilities.

Examples

Setup . webuse fitness . churdle linear hours age i.smoke distance i.single, select(commute whours) ll(0) het(age single) nolog

Fit conditional expectation the dependent variable hours . predict hourshat

Fit conditional expectation for values of the dependent variable greater than zero . predict exercises, e(0,.) . summarize hours hourshat exercises

Average marginal effect of single . margins, dydx(1.single)


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