*! version 1.1 Fernando Rios-Avila July 2019
help rifsureg

Title

rifsureg -- Seemingly unrelated recentered influence
function regression

Syntax

rifsureg depvar [indepvars] [if] [in] [weight],
qs(numlist) [options]

options

* qs(numlist) specify a list of numbers corresponding to the
quantiles of interest to be estimated; one can use any number between 0 and
100

bw(#) specify bandwidth

kernel(kernel) specify a specific kernel function

retain(str) specify a prefix for a new variable where the
generated recentered influence functions (RIFs) will be stored, based on the sample used in the regression; by
default, new variables are stored as __depvar_q##

replace when retain() is specified,
overwrite the variable retain(str) if it already exists

over(varname) indicate a variable over which the RIF will be
estimated; this can be understood as a partial conditional RIF; when the variable
used is binomial, the regression can be seen as the ordinary least-squares
alternative to Oaxaca-Blinder decomposition

rwlogit(varlist) specify the logit regression for the
estimation of the reweighting factors; the variable used in over() is
used as the dependent variable;
this can be used to obtain estimates akin to treatment effects under the assumption of exogeneity

rwprobit(varlist) specify the probit regression for the
estimation of the reweighting factors; the variable used in over() is
used as the dependent variable;
this can be used to obtain estimates akin to treatment effects under the assumption of exogeneity

rwmlogit(varlist) specify the mlogit regression for the
estimation of the reweighting factors; the variable used in over() is
used as the dependent variable;
this can be used to obtain estimates akin to multivalued treatment effects under
the assumption of exogeneity; only average treatment effects (ate) are allowed

rwmprobit(varlist) specify the mprobit regression for the
estimation of the reweighting factors; the variable used in over() is
used as the dependent variable;
this can be used to obtain estimates akin to multivalued treatment effects under
the assumption of exogeneity; only average treatment effects (ate) are
allowed

[ate|att|atu] indicate which estimator will be
obtained using the reweighted factors; the default is to estimate the average
treatment effect (ate);
one can also specify to obtain treatment effect on the treated (att) or
on the untreated (atu)

sureg_options most options in sureg can be used but have not been extensively tested

fweights and aweights are allowed. When using
rwlogit(varlist), rwprobit(varlist), rwmlogit(varlist), or
rwmprobit(varlist), weights are used as aweights; see
weight.

* qs() is required. The option qs(numlist) allows one to specify
all the quantiles of interest for the dependent variable. The RIF quantile
statistic is first obtained using the egen function
rifvar(), which is then used as the dependent variable in an
ordinary least-squares model that is fit using sureg.

Description

rifsureg is a wrapper command that uses the capabilities of sureg to estimate simultaneous RIF regressions for a set of quantiles.

The command fits the RIF-regression models in two steps. First, it estimates
the RIF for all the quantile statistics of interest using
rifvar(). Second, it uses the estimated RIF as the dependent
variable and fits the simultaneous RIF models using sureg. Using a
similar syntax to rifhdreg, rifsureg can be used to estimate
simultaneous regressions for treatment effects by using the option
over() in combination with rwlogit() or rwprobit() after
selecting the estimation of the type of treatment effects to be estimated
(Firpo and Pinto 2016). This can be thought of as the equivalent to
teffects, ipwra. This makes the estimator a double robust estimator for
treatment effects on distributional statistics under the assumption of
exogeneity of the treatment.

Because the command is a wrapper for sureg, most options from
this command are available but have not been fully tested.

For the correct estimation of bootstrap standard errors, it is recommended to
use the bootstrap prefix to apply the bootstrap through the whole
estimation process.

rifsureg typed without arguments replays the last results.

Examples

. webuse cattaneo2

Simultaneous RIF regressions across quantiles.

. rifsureg bweight mbsmoke prenatal1 mmarried mage fbaby, qs(5(10)95)

Simultaneous RIF regressions across quantiles. Treatment effects of
smoking.

. rifsureg bweight mbsmoke prenatal1 mmarried mage fbaby, qs(5(10)95) over(mbsmoke)

Simultaneous RIF regressions across quantiles. Treatment effects of smoking.
Using inverse-probability weighting with ate.

. rifsureg bweight mbsmoke prenatal1 mmarried mage fbaby, qs(5(10)95) over(mbsmoke) rwlogit(prenatal1 mmarried mage fbaby) ate

Simultaneous RIF regressions across quantiles. Treatment effects of smoking.
Using inverse-probability weighting with ate. Bootstrapped standard
errors.

. bootstrap: rifsureg bweight mbsmoke prenatal1 mmarried mage fbaby, qs(5(10)95) over(mbsmoke) rwlogit(prenatal1 mmarried mage fbaby) ate

Acknowledgments

This command is based on the community-contributed command rifreg.

RIF variables are estimated using the egen addon rifvar().

An intuitive description of RIF regressions is provided in Rios-Avila (2019).

All errors are my own.

References

Firpo, S. P., and C. Pinto. 2016. Identification and estimation of
distributional impacts of interventions using changes in inequality measures.
Journal of Applied Econometrics 31: 457-486. https://doi.org/10.1002/jae.2448.

Rios-Avila, F. 2020. Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition.
Stata Journal, 20(1), 51-94. https://doi.org/10.1177/1536867X20909690.

Author

Fernando Rios-Avila

Levy Economics Institute of Bard College

Annandale-on-Hudson, NY

friosavi@levy.org

Also see

Help: rifreg, rifhdreg, rifsureg, rifsureg2,
uqreg, hvar(), reghdfe, oaxaca_rif,
rifvar(), [R] sureg