Stata 15 help for ivtobit_postestimation

[R] ivtobit postestimation -- Postestimation tools for ivtobit

Postestimation commands

The following postestimation commands are of special interest after ivtobit:

Command Description ------------------------------------------------------------------------- estat correlation report the correlation matrix of the errors of the dependent variable and the endogenous variables estat covariance report the covariance matrix of the errors of the dependent variable and the endogenous variables ------------------------------------------------------------------------- These commands are not appropriate after the two-step estimator or the svy prefix.

The following standard postestimation commands are also available:

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; 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, forecast, and suest are not appropriate after ivtobit, twostep. + forecast, hausman, and lrtest are not appropriate with svy estimation results.

Syntax for predict

After ML or twostep

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

After ML

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

statistic Description ------------------------------------------------------------------------- Main xb linear prediction; the default stdp standard error of the linear prediction stdf standard error of the forecast; not available with two-step estimator pr(a,b) Pr(a < y < b) accounting for endogeneity; not available with two-step estimator e(a,b) E(y | a < y < b) accounting for endogeneity; not available with two-step estimator ystar(a,b) E(y*), y* = max{a,min(y,b)} accounting for endogeneity; not available with two-step estimator ------------------------------------------------------------------------- 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, and expected values.

Options for predict

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

xb, the default, calculates the linear prediction.

stdp calculates the standard error of the linear prediction. It 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. stdf is not available with the two-step estimator.

pr(a,b) calculates Pr(a < y < b | z), the probability that y|z would be observed in the interval (a,b) accounting for endogeneity.

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 | z); pr(lb,ub) calculates Pr(lb < y < ub | z); and pr(20,ub) calculates Pr(20 < y < ub | z).

a missing (a > .) means minus infinity; pr(.,30) calculates Pr(-infinity < y < 30 | z); pr(lb,30) calculates Pr(-infinity < y < 30 |z) in observations for which lb > . and calculates Pr(lb < y < 30 | z) elsewhere.

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

pr(a,b) is not available with the two-step estimator.

e(a,b) calculates E(y | a < y < b), the expected value of y|z conditional on y|z being in the interval (a,b), meaning that y|z is truncated. a and b are specified as they are for pr(). Endogeneity is accounted for when calculating e(a,b). e(a,b) is not available with the two-step estimator.

ystar(a,b) calculates E(y*), where y* = a if z + d < a, y* = b if z + d > b, and y* = z + d + u otherwise, meaning that y* is censored. a and b are specified as they are for pr(). Endogeneity is accounted for when calculating ystar(a,b). ystar(a,b) is not available with the two-step estimator.

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

For models with one endogenous regressor, five new variables are created.

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

The second new variable will contain the first derivative of the log likelihood with respect to the reduced-form equation for the endogenous regressor.

The third new variable will contain the first derivative of the log likelihood with respect to alpha.

The fourth new variable will contain the first derivative of the log likelihood with respect to ln(s).

The fifth new variable will contain the first derivative of the log likelihood with respect to ln(v).

For models with p endogenous regressors, p + {(p + 1)(p + 2)}/2 + 1 new variables are created.

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

The second through (p + 1)th new variables will contain the first derivatives of the log likelihood with respect to the reduced-form equations for the endogenous variables in the order they were specified when ivtobit was called.

The remaining score variables will contain the partial derivatives of the log likelihood with respect to the (p+1)(p+2)/2 ancillary parameters.

Syntax for margins

margins [marginlist] [, options]

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

statistic Description ------------------------------------------------------------------------- xb linear prediction; the default pr(a,b) Pr(a < y < b) accounting for endogeneity; not available with two-step estimator e(a,b) E(y | a < y < b) accounting for endogeneity; not available with two-step estimator ystar(a,b) E(y*), y* = max{a,min(y,b)} accounting for endogeneity; not available with two-step estimator stdp not allowed with margins stdf 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 linear predictions, probabilities, and expected values.

Syntax for estat

Correlation matrix

estat correlation [, border(bspec) left(#) format(%fmt)]

Covariance matrix

estat covariance [, border(bspec) left(#) format(%fmt)]

Menu for estat

Statistics > Postestimation

Description for estat

estat correlation displays the correlation matrix of the errors of the dependent variable and the endogenous variables.

estat covariance displays the covariance matrix of the errors of the dependent variable and the endogenous variables.

estat correlation and estat covariance are not allowed after the ivtobit two-step estimator.

Options for estat

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

border(bspec) sets border style of the matrix display. The default is border(all).

left(#) sets the left indent of the matrix display. The default is left(2).

format(%fmt) specifies the format for displaying the individual elements of the matrix. The default is format(%9.0g).

Examples

Setup . webuse laborsup . ivtobit fem_inc fem_educ kids (other_inc = male_educ), ll

Compute average marginal effects on expected income, conditional on it being greater than 10 (thousand dollars) . margins, predict(e(10,.)) dydx(other_inc fem_educ kids)

Estimate separately for women with 8, 12, and 16 years of education . margins, predict(e(10,.)) dydx(kids) at(fem_educ=(8(4)16))

Plot most recent estimates and confidence intervals . marginsplot


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