{smcl}
{* *! version 1.1 Fernando Rios-Avila July 2019}{...}
{cmd:help rifsureg2}
{hline}
{title:Title}
{p2colset 5 18 20 2}{...}
{p2col :{cmd:rifsureg2} {hline 2}}Seemingly unrelated recentered influence
function regression{p_end}
{p2colreset}{...}
{title:Syntax}
{p 8 17 2}
{cmd:rifsureg2} {depvar} [{indepvars}] {ifin} {weight}{cmd:,}
{opt rif(RIF_options)} [{it:options}]
{synoptset 25 tabbed}{...}
{marker opt}{synopthdr:options}
{synoptline}
{p2coldent :* {opt rif(RIF_options)}}specify a list of statistics for which
recentered influence functions (RIFs) will be estimated, including their statistic options, separated by a comma; see {helpb rifvar:rifvar()} for specifics{p_end}
{synopt :{opt retain(str)}}specify a prefix for a new variable where the
generated RIFs will be stored, based on the sample used in the regression;
by default, new variables are stored as {cmd:__}{it:depvar}{cmd:_m}{it:#}{p_end}
{synopt :{opt replace}}when {cmd:retain()} is specified,
overwrite the variable {opt retain(str)} if it already exists{p_end}
{synopt :{opt 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{p_end}
{synopt :{opt rwlogit(varlist)}}specify the {cmd:logit} regression for the
estimation of the reweighting factors; the variable used in {cmd:over()} is
used as the dependent variable;
this can be used to obtain estimates akin to treatment effects under the assumption of exogeneity{p_end}
{synopt :{opt rwprobit(varlist)}}specify the {cmd:probit} regression for the
estimation of the reweighting factors; the variable used in {cmd:over()} is
used as the dependent variable;
this can be used to obtain estimates akin to treatment effects under the assumption of exogeneity{p_end}
{synopt :{opt rwmlogit(varlist)}}specify the {cmd:mlogit} regression for the
estimation of the reweighting factors; the variable used in {cmd: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 ({cmd:ate}) are allowed{p_end}
{synopt :{opt rwmprobit(varlist)}}specify the {cmd:mprobit} regression for the
estimation of the reweighting factors; the variable used in {cmd: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 ({cmd:ate}) are
allowed{p_end}
{synopt :[{opt ate}|{cmd:att}|{cmd:atu}]}indicate which estimator will be obtained using
the reweighted factors; default is to estimate the average treatment
effect ({cmd:ate});
one can also specify to obtain the treatment effect on the treated ({cmd:att}) or
on the untreated ({cmd:atu}){p_end}
{synopt :{it:sureg_options}}most options in {helpb sureg} can be used but have not been extensively tested{p_end}
{synopt :{cmd:old}} This option request using the older {cmd:rifvar} function (for replication purposes). {p_end}
{synoptline}
{p 4 6 2}
{cmd:fweight}s and {cmd:aweight}s are allowed. When using
{opt rwlogit(varlist)}, {opt rwprobit(varlist)}, {opt rwmlogit(varlist)}, or
{opt rwmprobit(varlist)}, weights are used as {cmd:aweight}s; see
{help weight}.{p_end}
{p 4 6 2}
* {cmd:rif()} is required.
{title:Description}
{pstd}
{cmd:rifsureg2} is a wrapper command that uses the capabilities of {helpb sureg}
to estimate simultaneous RIF regressions.
{pstd}
The command fits the RIF regression models in two steps. First, it estimates
the RIF for all the statistics of interest using {helpb rifvar:rifvar()}.
Second, it uses the estimated RIF as dependent variable and fits the
simultaneous RIF models using {helpb sureg}. Using a similar syntax to
{helpb rifhdreg}, {cmd:rifsureg2} estimates simultaneous regressions for
treatment effects by using the option {cmd:over()} in combination with
{cmd:rwlogit()} or {cmd:rwprobit()} after selecting the estimation of the type
of treatment effects to be estimated (Firpo and Pinto 2016).
{pstd}
Because the command is a wrapper for {cmd:sureg}, most options from
{cmd:sureg} are available but have not been fully tested.
{pstd}
For the correct estimation of bootstrap standard errors, it is recommended to
use the {cmd:bootstrap} prefix to apply the bootstrap through the whole
estimation process.
{pstd}
{cmd:rifsureg2} typed without arguments replays the last results.
{title:Examples}
{phang2}
{bf:. {stata "webuse cattaneo2"}}
{pstd}
Simultaneous RIF regressions across quantiles.{p_end}
{phang2}
{bf:. {stata rifsureg2 bweight mbsmoke prenatal1 mmarried mage fbaby, rif(q(10),q(90),std)}}
{pstd}
Simultaneous RIF regressions across quantiles. Treatment effects of
smoking.{p_end}
{phang2}
{bf:. {stata rifsureg2 bweight mbsmoke prenatal1 mmarried mage fbaby, rif(q(10),q(90),std) over(mbsmoke)}}
{pstd}
Simultaneous RIF regressions across quantiles. Treatment effects of
smoking. Using inverse-probability weighting with {cmd:ate}.{p_end}
{phang2}
{bf:. {stata rifsureg2 bweight mbsmoke prenatal1 mmarried mage fbaby, rif(q(10),q(90),std) over(mbsmoke) rwlogit(prenatal1 mmarried mage fbaby) ate}}
{pstd}
Simultaneous RIF regressions across quantiles. Treatment effects of
smoking. Using inverse-probability weighting with {cmd:ate}. Bootstrapped
standard errors.{p_end}
{phang2}
{bf:. {stata "bootstrap: rifsureg2 bweight mbsmoke prenatal1 mmarried mage fbaby, rif(q(10),q(90),std) over(mbsmoke) rwlogit(prenatal1 mmarried mage fbaby) ate"}}
{marker Acknowledgments}{...}
{title:Acknowledgments}
{pstd}
This command is based on the community-contributed command {cmd:rifreg}.
{pstd}
RIF variables are estimated using the {cmd:egen} add-on {cmd:rifvar()}. An
intuitive description of RIF regressions is provided in Rios-Avila (2020).
{pstd}
All errors are my own.
{title:References}
{phang}
Firpo, S. P., and C. Pinto. 2016. Identification and estimation of
distributional impacts of interventions using changes in inequality measures.
{it:Journal of Applied Econometrics} 31: 457-486.
{browse "https://doi.org/10.1002/jae.2448"}.
{phang}
Rios-Avila, F. 2020. Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition.
Stata Journal, 20(1), 51-94. {browse "https://doi.org/10.1177/1536867X20909690"}.
{marker Author}{...}
{title:Author}
{pstd}
Fernando Rios-Avila{break}
Levy Economics Institute of Bard College{break}
Annandale-on-Hudson, NY{break}
friosavi@levy.org
{title:Also see}
{p 4 14 2}
{p 7 14 2}
Help: {helpb rifreg}, {helpb reghdfe}, {helpb oaxaca_rif},
{helpb rifvar:rifvar()}, {helpb rifhdreg}, {helpb rifsureg}, {helpb uqreg},
{helpb hvar:hvar()} (if installed), {manhelp sureg R}{p_end}