help suest dialog: suest
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Title
[R] suest -- Seemingly unrelated estimation
Syntax
suest namelist [, options]
where namelist is a list of one or more names under which estimation
results were saved via estimates store. Wildcards may be used. *
and _all refer to all stored results. A single period (.) may be
used to refer to the last estimation results, even if they have not
(yet) been stored.
options description
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SE/Robust
svy survey data estimation
vce(vcetype) vcetype may be robust or cluster clustvar
Reporting
level(#) set confidence level; default is level(95)
dir display a table describing the models
eform(string) report exponentiated coefficients and label as
string
display_options control spacing and display of omitted variables
and base and empty cells
+ coeflegend display coefficients' legend instead of coefficient
table
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+ coeflegend does not appear in the dialog box.
Menu
Statistics > Postestimation > Tests > Seemingly unrelated estimation
Description
suest is a postestimation command; see estcom and postest.
suest combines the estimation results -- parameter estimates and
associated (co)variance matrices -- stored under namelist into one
parameter vector and simultaneous (co)variance matrix of the
sandwich/robust type. This (co)variance matrix is appropriate even if
the estimates were obtained on the same or on overlapping data.
Typical applications of suest are tests for intramodel and cross-model
hypotheses using test or testnl, for example, a generalized Hausman
specification test. lincom and nlcom may be used after suest to estimate
linear combinations and nonlinear functions of coefficients. suest may
also be used to adjust a standard VCE for clustering or survey design
effects.
Different estimators are allowed, for example, a regress model and a
probit model; the only requirement is that predict produce equation-level
scores with the score option after an estimation command. The models may
be estimated on different samples, due either to explicit if or in
selection or to missing values. If weights are applied, the same weights
(type and values) should be applied to all models in namelist. The
estimators should be estimated without vce(robust) or vce(cluster
clustvar) options. suest returns the robust VCE, allows the vce(cluster
clustvar) option, and automatically works with results from the svy
prefix command (only for vce(linearized)).
Because suest posts its results like a proper estimation command, its
results can be stored via estimates store. Moreover, like other
estimation commands, suest typed without arguments replays the results.
Options
+-----------+
----+ SE/Robust +--------------------------------------------------------
svy specifies that estimation results should be modified to reflect the
survey design effects according to the svyset specifications; see
[SVY] svyset.
The svy option is implied when suest encounters survey estimation
results from the svy prefix. Poststratification is allowed only with
survey estimation results from the svy prefix.
vce(vcetype) specifies the type of standard error reported, which
includes types that are robust to some kinds of misspecification and
that allow for intragroup correlation; see [R] vce_option.
The vce() option may not be combined with the svy option or
estimation results from the svy prefix.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#) specifies the confidence level, as a percentage, for confidence
intervals of the coefficients; see [R] level.
dir displays a table describing the models in namelist just like
estimates dir namelist.
eform(str) displays the coefficient table in exponentiated form: for
each coefficient, exp(b) rather than b is displayed, and standard
errors and confidence intervals are transformed. Display of the
intercept, if any, is suppressed. str is the table header that will
be displayed above the transformed coefficients and must be 11
characters or fewer, for example, eform("Odds ratio").
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
The following option is available with suest but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Using suest
If you plan to use suest, you must take precautions when fitting the
original models. These restrictions are relaxed when using survey data;
see Using suest with survey data below.
1. suest works with estimation commands that allow predict to
generate equation-level score variables when supplied with the
score (or scores) option. For example, equation-level score
variables are generated after running mlogit by typing
. predict sc*, scores
2. Estimation should take place without the vce(robust) or
vce(cluster clustvar) options. suest always computes the robust
estimator of the (co)variance, and suest has a vce(cluster
clustvar) option.
The within-model covariance matrices computed by suest are
identical to those obtained by specifying a vce(robust) or
vce(cluster clustvar) option during estimation. suest, however,
also estimates the between-model covariances of parameter
estimates.
3. Finally, the estimation results to be combined should be stored
by estimates store; see [R] estimates store.
After estimating and storing a series of estimation results, you are
ready to combine the estimation results with suest,
. suest name1 [name2 ...] [, vce(cluster clustvar)]
and you can subsequently use postestimation commands, such as test, to
test hypotheses. Here an important issue is how suest assigns names to
the equations. If you specify one model name, the original equation
names are left unchanged; otherwise, suest constructs new equation names.
The coefficients of a single-equation model (such as logit and poisson)
that was estimate stored under name X are collected under equation X.
With a multiequation model stored under name X, suest prefixes X_ to an
original equation name eq, forming equation name X_eq.
Technical note: in rare circumstances, suest may have to truncate
equation names to 32 characters. When equation names are not unique
because of truncation, suest numbers the equations within models, using
equations named X_#.
Using suest with survey data
suest can be used to obtain the variance estimates for a series of
estimators that used the svy prefix. To use suest for this purpose,
perform the following steps:
1. Be sure to set the survey design characteristics correctly using
svyset. Do not use the vce() option to change the default
variance estimator from the linearized variance estimator.
vce(brr) and vce(jackknife) are not supported by suest.
2. Fit the model or models by using the svy prefix command,
optionally including the subpopulation with the subpop() option.
3. Store the estimation results with estimates store name.
4. Use suest with name.
Remarks on specific commands
Some estimation commands store or name their results in a slightly
nonstandard way, mostly for historical reasons. suest provides "fixes"
in these cases.
regress does not include its ancillary parameter, the residual variance,
in its coefficient vector and (co)variance matrix. Moreover, while
the score option is allowed with predict after regress, a score
variable is generated for the mean but not for the variance
parameter. suest contains special code that assigns the equation
name mean to the coefficients for the mean, adds the equation lnvar
for the log variance, and computes the appropriate score variables.
Example 1: A Hausman test
. webuse sysdsn4
. mlogit insure age male
. estimates store m1, title(all three insurance forms)
. quietly mlogit insure age male if insure != "Uninsure":insure
. estimates store m2, title(insure != "Uninsure":insure)
. quietly mlogit insure age male if insure != "Prepaid":insure
. estimates store m3, title(insure != "Prepaid":insure)
. hausman m2 m1, alleqs constant
. hausman m3 m1, alleqs constant
Example 2: Do coefficients vary between groups? ("Chow test")
. webuse income
. regress inc edu exp if male
. estimates store Male
. regress inc edu exp if !male
. estimates store Female
. suest Male Female
. test [Male_mean = Female_mean]
Example 3: A nonlinear Hausman-like test
. webuse income
. probit promo edu exp
. estimates store Promo
. regress inc edu exp
. estimates store Inc
. suest Promo Inc
. testnl [Promo]edu/[Promo]exp = [Inc_mean]edu/[Inc_mean]exp
Example 4: Using suest with survey data, the svy prefix
. webuse nhanes2f, clear
. svyset psuid [pw=finalwgt], strata(stratid)
. svy: ologit health female black age age2
. estimates store H5
. gen health3 = clip(health,2,4)
. svy: ologit health3 female black age age2
. estimates store H3
. suest H5 H3
. test [H5_health=H3_health3]
. test ([H5_cut2]_cons=[H3_cut1]_cons) ([H5_cut3]_cons=[H3_cut2]_cons)
Example 5: Using suest with survey data, the svy option
. webuse nhanes2f, clear
. svyset psuid [pw=finalwgt], strata(stratid)
. ologit health female black age age2 [iw=finalwgt]
. estimates store H5
. gen health3 = clip(health,2,4)
. ologit health3 female black age age2 [iw=finalwgt]
. estimates store H3
. suest H5 H3, svy
. test [H5_health=H3_health3]
. test ([H5_cut2]_cons=[H3_cut1]_cons) ([H5_cut3]_cons=[H3_cut2]_cons)
Saved results
suest saves the following in e():
Scalars
e(N) number of observations
e(N_clust) number of clusters
e(rank) rank of e(V)
Macros
e(cmd) suest
e(eqnames#) original names of equations of model #
e(names) list of model names
e(wtype) weight type
e(wexp) weight expression
e(clustvar) name of cluster variable
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
Matrices
e(b) stacked coefficient vector of the models
e(V) (co)variance matrix
Functions
e(sample) marks estimation sample
Also see
Manual: [R] suest
Help: [R] estimates, [R] hausman, [R] lincom, [R] nlcom, [P] _robust,
[R] test, [R] testnl
