Title

khb Table of KHB-corrected coefficients of nested non-linear probabiltiy models

Syntax

khbtab
model-type
depvar varlist || varlist [ || varlist || ... ]
[ using filename ] [if] [in]
[weight]
[ , options ]

Main
nopreserve keep residuals and estimates

prefix(prefix_cmd) prefix models with bootstrap, svy, etc ...

KHB-options
vce(vcetype) vcetype may be robust, cluster
clustvar

ape display average partial (marginal) effects

continuous treat dummy variables as continuous when ape

Reporting options
esttab use esttab instad of estimates table

tableoptions(...) options allowed for estimates table or esttab

model-type can be any of regress, logit,
ologit, probit, oprobit, cloglog, slogit,
scobit, rologit, clogit, xtlogit,
xtprobit and mlogit. Other models might also produce
output but for the time being this output is considered to be
"experimental".

Factor variables are allowed for Stata 12 or
higher. Factor variables are allowed.

aweights, fweights, iweights, and pweights are allowed if they
are allowed in the specified model-type; see weight.

Description

khbtab creates a table of nested non-linear probability
models with all coefficients expressed in the scale of the most
saturated model (see help khb). Basically, the command allows
the user to insert pipe symbols (||) to separate a model with many
independend variables into layers of nested models. For example

. use dlsy_khb.dta

. khbtab logit univ intact boy || fses || abil

creates a table of the regression coefficients of the following
logistic regression models:

. logit univ intact boy

. logit univ intact boy fses

. logit univ intact boy fses abil

The KHB method is used to rescale all coefficients in the
table to the scale of the most saturated model, i.e. to the scale of
the last model.

Options

nopreserve By default, khbtab leaves the dataset
unchanged, i.e. it does not keep the created residuals stored
estimates. With option nopreserve residuals and the results of
the estimated models remain in the dataset.

prefix(prefix_cmd) allows to specify the following
prefix commands to be used in front of each of the regression
commands:

o bootstrap

o jackknife

o svy

o svy linearized

o svy bootstrap

o svy jackknife

o svy brr

o svy sdr

Note that all prefixes with svy require appropriate setting
of the dataset; see help svyset. Note also that any weights
defined with svyset will be used automatically if prefix
svy is being used. The user must not specifiy further weights in
this case.

vce(vcetype) specifies the type of standard error
reported; see help vce_option. It defaults to the Stata's
defaults for the specified model-type.

ape displays average partial effects (average marginal
effects) instead of coeficients. For model-types ologit
and oprobit, khb uses the average partial effect on the
probability for the first outcome. This can be changed with option
predict().

continuous Average partial effects are by default based
on unit effects for dummy variables. Specifying continuous treats
dummy variables equal to continuous variables. See margins
for details about this option

outcome() lets the user specify the outcome for which the
average partial (marginal) effects are calculated. The default is
the outcome with the lowest level.

esttab uses the user written command esttab (Ben
Jann) to create the output. By default estimates table is
being used. Note that esttab must be installed before using
this option. Note as an aside that further esttab options should be
placed in option tableoptions().

tableoptions() lets the users specify all options that
are allowed with estimates table. If option esttab is
being used, tableoptions() lets the user specify all options
that are allowed for esttab. Note specifically, that the
display of exponentiated coefficients and equations of
multi-equation models is handled by tableoptions.

Example(s)

. use dlsy_khb.dta

. khbtab logit univ intact boy || fses || abil

. khbtab logit univ intact boy || fses || abil, tableoptions(eform stats(r2_p N))

. khbtab ologit fgroup fses edu || univ || abil

. khbtab ologit fgroup fses edu || univ || abil, ape outcome(2)

. khbtab mlogit fgroup fses edu || univ || abil

. khbtab mlogit fgroup fses edu || univ || abil, tableoptions(drop("Middle:"))

. khbtab mlogit fgroup fses edu || univ || abil, ape outcome("Highest")

References

Breen, R./Karlson, K.B./Holm, A. (2013).
Total, direct, and indirect effects in logit models.
Sociological Methods and Research, 42(2)164-191.

Karlson, K.B./Holm, A./Breen, R. (2011): Comparing Regression
Coefficients Between Same-sample Nested Models using Logit and Probit. A New
Method. Sociological Methodology 42:286-313.

Karlson, K.B./Holm, A. (2011): Decomposing
primary and secondary effects: A new decomposition method. Research
in Stratification and Social Mobility 29:221-237.

Kohler, U./Karlson, K.B./Holm, A. (2011): Comparing
coefficients of nested nonlinear probability models. The Stata Journal
11:420-438.

Also see

Manual: [R] margins

Online: help for margins, ldecomp (if installed)

Web: Stata's Home

Author

Ulrich Kohler (ukohler@uni-potsdam.de)