## Stata 15 help for estcom

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Title

[U] 20 Estimation and postestimation commands

Description

For a list of Stata's estimation commands, see estimation commands.  For
a discussion of postestimation commands, see postest.

Properties shared by all estimation commands are listed.

Remarks

Stata commands that fit statistical models -- commands such as logit,
regress, logistic, and sureg -- work similarly.

Remarks are presented under the following headings:

1. Common syntax
2. Estimation on subsamples
3. Robust variance estimates
4. Prefix commands
5. Confidence intervals of parameters
6. Format of coefficient table
7. Tests of parameters
8. Point estimates and CIs of linear and nonlinear combinations
9. Predictions
10. Forecasts
11. Generalized predictions
12. Marginal means, predictive margins, marginal effects, and average
marginal effects
13. Plots of margins
14. Contrasts
15. Pairwise comparisons
16. Estimation statistics
17. Variance-covariance matrix of the estimators (VCE)
18. Coefficients and standard errors in expressions
19. Managing and combining estimates
20. Redisplaying estimates
21. Factor rotations
22. Specialized graphs
23. Postestimation Selector

1. Common syntax

Single-equation estimation commands usually have syntax

command varlist [if] [in] [weight] [, options]

and multiple-equation estimation commands usually have syntax

command (varlist) ...  (varlist) [if] [in] [weight] [, options]

In single-equation commands, the first variable in varlist is the
dependent variable and the remaining are the independent variables,
although there can be variations.  For instance, anova allows you to
specify both variables and terms as independent variables.

2. Estimation on subsamples

You can use Stata's standard syntax (if and in) to restrict the sample;
you do not have to make a special dataset.

3. Robust variance estimates

Most estimation commands allow option vce(robust), which provides the
Huber/White/sandwich estimator of variance.  Those that do also provide
option vce(cluster clustvar), which relaxes the assumption of
independence.  See [U] 20.22 Obtaining robust variance estimates.

4. Prefix commands

Prefix commands may be used to modify or extend what the estimation
command does.  The syntax is

prefix_command ... : command ...

where

prefix_command        Description
---------------------------------------------------------------------
statsby:              collect results across subsets of the data
rolling:              collect statistics on moving subsets

bootstrap:            bootstrap estimation
jackknife:            jackknife estimation

* svy:                  estimation with complex survey data
* mi estimate:          run command on multiply imputed data and adjust
results for multiple imputation (MI)

* bayes:                fit model as a Bayesian regression

* fmm:                  fit model using finite mixture modeling

* nestreg:              run command with accumulated blocks of
regressors and report nested model comparison
tests
* stepwise:             stepwise estimation

* fp:                   run command with fractional polynomials of one
regressor
* mfp:                  run command with multiple fractional polynomial
regressors
---------------------------------------------------------------------
* Available for most but not all estimation commands.
See help prefix for a full list of prefix_commands.

Before using the bootstrap: or jackknife: prefixes, however, check
whether the estimation command allows option vce(bootstrap) or
vce(jackknife).  If it does, using the option rather than the prefix is
better.  The option is implemented in terms of the prefix command, but
the option automatically knows to pass all the appropriate suboptions for
the specific estimator you are using.

5. Confidence intervals of parameters

Estimation commands display confidence intervals of the coefficients.
Estimation-command option level() specifies the width of the interval.
The default is level(95), meaning 95% confidence intervals.

You can reset the default with set level.

6. Format of coefficient table

You can change the formatting of test statistics, p-values, coefficients,
standard errors, and confidence limits in the coefficient table. See [U]
20.9 Formatting the coefficient table.

7. Tests of parameters

You can perform tests on the estimated parameters by using

o  test -- Wald test of linear hypotheses

o  testnl -- Wald test of nonlinear hypotheses

o  lrtest -- likelihood-ratio tests

o  hausman -- Hausman specification test

o  suest -- generalization of the Hausman test

8. Point estimates and CIs of linear and nonlinear combinations

You can obtain point estimates and confidence intervals of linear
combinations of the estimated parameters by using lincom, and those of
nonlinear combinations by using nlcom.

9. Predictions

You can obtain predictions, residuals, influence statistics, and the
like, either for the data on which you just estimated or for some other
data, by using predict.

The help for predict is found in two places:

1.  help predict -- general information

2.  help estimation_command postestimation -- specific information
and special features following estimation by estimation_command.
For instance, help regress postestimation tells you about predict
following regress.

The easy way to access the postestimation help is to see [R] regress (or
whatever estimation command you are using) and then select
postestimation.

10. Forecasts

You can combine multiple estimation results and other equations to obtain
time-series forecasts; see [TS] forecast.

11. Generalized predictions

You can obtain nonlinear predictions, standard errors, Wald test
statistics, significance levels, and confidence intervals, either for the
data on which you just estimated or for some other data, by using
predictnl.

One especially useful feature of predictnl is that you can obtain
standard errors for most predictions available via predict, and you can
obtain standard errors of functions and combinations of these
predictions.

12. Marginal means, predictive margins, marginal effects, and average
marginal effects

Command margins estimates marginal means, adjusted predictions, marginal
effects, partial effects, or other expressions at fixed values for the
regressors; or it estimates averages of means, adjusted predictions,
marginal effects, partial effects, or other expressions at fixed values
of some covariates and averaging over the rest.  Averages are based on
the data currently in memory.

13. Plots of margins

Command marginsplot graphs the results of the immediately preceding
margins command.

14. Contrasts

The postestimation command contrast estimates and tests contrasts.
Included are ANOVA-style tests of main effects, simple effects,
interaction effects, and nested effects.  You may use the built-in
contrast operators, or define your own custom contrasts.

The command margins, contrast extends contrast to margins of linear and
nonlinear responses.

15. Pairwise comparisons

The postestimation command pwcompare performs pairwise comparisons across
the levels of factor variables.  The resulting tests and confidence
intervals may be adjusted for multiple comparisons.

The command margins, pwcompare extends pwcompare to margins of linear and
nonlinear responses.

To perform pairwise comparisons of means, use pwmean.

16. Estimation statistics

Command estat ic displays scalar- and matrix-valued postestimation
statistics such as AIC and BIC.

17. Variance-covariance matrix of the estimators (VCE)

Command estat vce displays the VCE -- either as a covariance matrix or as
a correlation matrix.

Estimation commands store coefficients in the matrix e(b) and the VCE in
e(V).

You can obtain the coefficients and VCE into Mata matrices by using
st_matrix("e(b)") and st_matrix("e(V)"); see [M-5] st_matrix().

18. Coefficients and standard errors in expressions

You can refer to the coefficients and standard errors in expressions by
using _b[name] and _se[name], such as

. generate contribution = _b[mpg]*mpg

See [U] 13.5 Accessing coefficients and standard errors and see _b.

19. Managing and combining estimates

You can store estimation results with command estimates store.  These
estimation results may later be restored and replayed, the coefficients
of one or more may be combined in a table, etc.; see [R] estimates.

Programmers should also see command [P] _estimates, which is a low-level
tool that manages stored estimation results.

20. Redisplaying estimates

You can, at any time, review your most recent estimates by typing the
estimation command without arguments.

21. Factor rotations

You can rotate loadings after factorlike commands; see [MV] rotate.

22. Specialized graphs

There are specialized graph commands available after some estimation
commands.

For instance, command lroc will graph the ROC curve after logistic,
logit, probit, or ivprobit.  Command screeplot will make scree plots
after factor or pca, as well as various other multivariate commands.
Command stcurve will plot the survivor, hazard, or cumulative hazard
function after stcox, stintreg, streg, mestreg, or xtstreg and will plot
the cumulative subhazard or cumulative incidence function after stcrreg.

What is available can always be found in the postestimation section of
the documentation following the estimator.

23. Postestimation selector

Launch the Postestimation Selector window to see a list of all
postestimation features that are available for the currently active
estimation results.  You can launch the dialog box for an item in the
list.  The list is automatically updated when estimation commands are run
or estimates are restored from memory or disk.  See [R] postest.

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