TitlePotential outcome parameter estimation

poparms(treatvargpsvars) (depvarcvars) [if] [in] [,options]

optionsDescription -------------------------------------------------------------------------quantiles(numlist) estimate specified quantiles

vce(vcetype [,vceoptions])vcetypemay bebootstrap,analytic, ornone.analyticis the default whenquantiles()is not specified.bootstrapis the default whenquantiles()is specified.vceoptionsvary overvcetypeand are discussed below.

ipwuse inverse-probability-weighted (IPW) estimator instead of default efficient-influence-function (EIF) estimatorINCLUDE help shortdes-coeflegend

-------------------------------------------------------------------------

gpsvarsandcvarsmay contain time-series operators; see fvvarlist.

poparmsestimates parameters of the potential-outcome distributions in causal inference.The estimators implemented in

poparmswere derived in Cattaneo(2010). Cattaneo, Drukker, and Holland (2012) provides an introduction to this command.

quantiles()specifies the quantiles of the potential outcome distributions that are to be estimated jointly with the means. By default, only the means are estimated. By default, methodvce(bootstrap)is used whenquantiles()is specified. We strongly recommend not usingvce(analytic)whenquantiles()is specified.

vce()specifies the method used to estimate the variance-covariance of the estimator.

vce:(vcetype[,vceoptions])} specifies thevcetypeand the type specific options.When specifying

vce(bootstrap), thevceoptionisreps(#)which specifies the number of bootstrap repetitions which must be integer that is at least 50.With method

analytic, thevceoptionsarebwscale(#)),bwidths(matname), anddensities(matname). These suboptions are mutually exclusive.By default,

poparmsuses an analytic estimator when only means are estimated and it uses a bootstrap estimator when quantiles are estimated. We recommend not using the analytic method when quantiles are specified because this method performed poorly in Monte Carlo simulations.With method

bootstrap, you may change the number of repetitions from the default 2000 by specifyingvce(bootstrap , reps(#)). The specified number of repetitions must an integer greater than 49.With method

analytic, you may rescale the bandwidths used to estimate the densities by specifyingvce(analytic, bwscale(#)). The specified number must be in the interval [.1, 10].With method

analytic, you may specify the bandwidths used to estimate the densities by specifyingvce(analytic,bwidths(matname)), wherematnamespecifies a Stata row vector with the number of columns equal to the number of quantiles times the number of treatment levels.With method

analytic, you may specify the densities usedvce(analytic, densities(matname)), wherematnamespecifies a Stata row vector with the number of columns equal to the number of quantiles times the number of treatment levels.

ipwspecifies thatpoparmsuse the IPW estimator instead of the default EIF estimator. The methods and differences are described in Cattaneo, Drukker, and Holland (2012).

coeflegend; see[R] estimation options.

--------------------------------------------------------------------------- Setup

. use spmdataMean estimation

. poparms (w pindex eindex) (spmeasure pindex eindex)Mean estimation with polynomial for conditional mean

. poparms (w pindex eindex) (spmeasure c.(pindex eindex)#c.(pindexeindex))Mean and quantile estimation with polynomial for conditional mean This example limits the number of of bootstrap repetitions to 50 so that the example runs relatively quickly. We recommend using at least the default of 2000 repetitions in practice.

. poparms (w pindex eindex) (spmeasure c.(pindex eindex)#c.(pindexeindex)), quantiles(.25 .75) vce(bootstrap, reps(50))

poparmssaves the following ine():Scalars

e(N)number of observationse(k)number of variables in conditional meane(bwscale)scale for bandwidths, if specifiede(reps)number of requested bootstrap repetitions, if specified}e(bsreps)number of successful bootstrap repetitions, if specified}Macros

e(cmd)poparmse(cmdline)command as typede(depvar)name of dependent variablee(title)title in estimation outpute(title2)second title in estimation outpute(vce)vcetypespecified invce()e(vcetype)title used to label Std. Err.e(quantiles)specified quantilese(properties)b Vorbifvce(none)e(predict)program used to implementpredictMatrices

e(b)coefficient vectore(V)variance-covariance matrix of the estimatorse(V1)outer product Psi functions used in varianceFunctions

e(sample)marks estimation sample

ReferencesCattaneo, M. D. 2010. Efficient Semiparametric Estimation of Multi-valued Treatment Effects under Ignorability. Journal of Econometrics 155(2): 138-154. http://www.sciencedirect.com/science/article/pii/S030440760900236X

Cattaneo, M. D., D. M. Drukker, and A. Holland. 2012. Estimation of multivalued treatment effects under conditional independence. Working paper, University of Michigan, Department of Economics, http://www-personal.umich.edu/~cattaneo/papers/Cattaneo-Drukker-Holla > nd_2012_STATA.pdf.

AuthorsMatias D. Cattaneo, University of Michigan, Ann Arbor, MI. cattaneo@umich.edu.

David M. Drukker, StataCorp, College Station, TX. ddrukker@stata.com.