Title

                  Potential outcome parameter estimation


Syntax

        poparms (treatvar gpsvars) (depvar cvars) [if] [in] [, options]

    options                       Description
    -------------------------------------------------------------------------
      quantiles(numlist)          estimate specified quantiles

      vce(vcetype [, vceoptions]) vcetype may be bootstrap, analytic, or
                                    none.
                                  analytic is the default when quantiles() is
                                  not specified.  bootstrap is the default
                                  when quantiles() is specified.
                                  vceoptions vary over vcetype and are
                                  discussed below.

      ipw                         use inverse-probability-weighted (IPW)
                                    estimator instead of default
                                    efficient-influence-function (EIF)
                                    estimator

INCLUDE help shortdes-coeflegend

    -------------------------------------------------------------------------
    gpsvars and cvars may contain time-series operators; see fvvarlist.

Description

    poparms estimates parameters of the potential-outcome distributions in
    causal inference.

    The estimators implemented in poparms were derived in Cattaneo(2010).
    Cattaneo, Drukker, and Holland (2012) provides an introduction to this
    command.


Options

    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, method
        vce(bootstrap) is used when quantiles() is specified.  We strongly
        recommend not using vce(analytic) when quantiles() is specified.

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

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

          When specifying vce(bootstrap), the vceoption is reps(#) which
            specifies the number of bootstrap repetitions which must be
            integer that is at least 50.

          With method analytic, the vceoptions are bwscale(#)),
            bwidths(matname), and densities(matname).  These suboptions are
            mutually exclusive.

          By default, poparms uses 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 specifying vce(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 specifying vce(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 specifying vce(analytic,
              bwidths(matname)), where matname specifies 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 used
              vce(analytic, densities(matname)), where matname specifies a
              Stata row vector with the number of columns equal to the number
              of quantiles times the number of treatment levels.

    ipw specifies that poparms use 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.


Examples

    ---------------------------------------------------------------------------
    Setup
        . use spmdata

    Mean estimation
        . poparms (w pindex eindex) (spmeasure pindex eindex)

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

    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.(pindex
            eindex)), quantiles(.25 .75) vce(bootstrap, reps(50))

Saved results

    poparms saves the following in e():

    Scalars        
      e(N)                number of observations
      e(k)                number of variables in conditional mean
      e(bwscale)          scale for bandwidths, if specified
      e(reps)             number of requested bootstrap repetitions, if
                            specified}
      e(bsreps)           number of successful bootstrap repetitions, if
                            specified}

    Macros         
      e(cmd)              poparms
      e(cmdline)          command as typed
      e(depvar)           name of dependent variable
      e(title)            title in estimation output
      e(title2)           second title in estimation output
      e(vce)              vcetype specified in vce()
      e(vcetype)          title used to label Std. Err.
      e(quantiles)        specified quantiles
      e(properties)       b V or b if vce(none)
      e(predict)          program used to implement predict

    Matrices       
      e(b)                coefficient vector
      e(V)                variance-covariance matrix of the estimators
      e(V1)               outer product Psi functions used in variance

    Functions      
      e(sample)           marks estimation sample


References

    Cattaneo, 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.


Authors

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

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