help semipar                             (Vincenzo Verardi and Nicolas Debarsy)
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Title

    semipar

Syntax

    Robinson's (1988) semiparametric regression estimator

        semipar varlist [if] [in] [weight], nonpar(varname) [options]



    options              Description
    -------------------------------------------------------------------------
    Model
      nonpar(varname)    Specifies the variable that enters the model
                           nonlinearly
      generate(varname)  Generate the nonparametric fit of the dependent
                           variable
      partial(varname)   Generate the dependent variable partialled out from
                           the parametric fit
      degree(#)          Specifies the degree of the local weighted
                           polynomial fit used in the kernel; default is 1
                           (see lpoly)
      trim(#)            Specifies the level of trimming for the pdf of the
                           variable entering the model nonlinearly; default
                           is 0 (no trimming)
      kernel(kernel)     Specifies kernel function; default is
                           kernel(gaussian)
 
    -------------------------------------------------------------------------
    Kernels
      gaussian            Gaussian kernel function, the default
      epanechnikov        Epanechnikov kernel function
      epan2               Alternative epanechnikov kernel function
      biweight            Biweight kernel function
      cosine              Cosine trace kernel function
      parzen              Parzen kernel function
      rectangle           Rectangle kernel function
      triangle            Triangle kernel function
    -------------------------------------------------------------------------
    Graphics
      nograph            Prevents from showing the graph of the nonparametric
                           fit
      ci                 Shows the confidence interval around the
                           nonparametric fit
      title()            Specifies the title of the graph for the
                           nonparametric fit
      ytitle()           Specifies the label of y-axis in the graph of the
                           nonparametric fit
      xtitle()           Specifies the label of x-axis in the graph of the
                           nonparametric fit
    -------------------------------------------------------------------------
    Standard errors
      robust             Uses the sandwich variance formula to compute
                           standard errors of the estimated parameters
  
      cluster(varname)   Computes clustered-corrected standard errors of the
                           estimated parameters
    -------------------------------------------------------------------------
    Specification test
      test(#)            Computes Hardle and Mammen's (1993) specification
                           test to assess if the nonparametric fit can be
                           approximated by a parametric adjustment of order
                           (#). With the cluster option specified, bootstrap
                           sample of clusters are drawn

      nsim(#)            Specifies the number of bootstrap replicates to be
                           done to do inference on the test; default is 100

      weight_test()      Allows to weight the distance between the
                           nonparametric and parametric fits for the test;
                           default is 1/n.

      level(#)           Specifies the level of confidence for inference;
                           default is level(95)
    -------------------------------------------------------------------------
    fweights and aweights are allowed; see weight

Description

    semipar estimates the Robinson's (1988) double residual estimator and
    estimates the nonlinear relation between the variable set in nonpar and
    the dependent variable.  The nonparametric estimator used is a gaussian
    kernel weighted local polynomial fit.

    Besides, the test option allows the user to assess whether a polynomial
    adjustment could be used to approximate the nonparametric fit.



Examples

        . use http://fmwww.bc.edu/ec-p/data/wooldridge/HPRICE3
        . gen lprice =log(price)
        . semipar lprice ldist larea lland rooms bath age, nonpar(linst)
            xtitle(linst) ci


Same as above, but testing for the appropriateness of a polynomial adjustment o
> f order 2 for linst

        . semipar lprice ldist larea lland rooms bath age, nonpar(linst)
            xtitle(linst) ci test(2)


References

Hardle W., E. Mammen (1993), Comparing nonparametric versus parametric regressi
> on fits, 
Annals of Statistics, 21, 1926-1947.

Robinson P.M. (1988), Root-N consistent semiparametric regression, Econometrica
> , 56, 931-954.