```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
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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)

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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
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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
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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
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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)
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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.
```