Implements the Klein-Spady estimator for a
semiparametric binary-choice model of the form
D = 1(X*beta + epsilon >= 0).
The distribution of the error is left unspecified
and estimated using kernel or local linear
regression. The program returns coefficient
estimates, marginal effects, and the error density
evaluated at the index mean.
Implements the Ichimura estimator for the
semiparametric regression model
Y = g(X*b) + e using kernel regression
with the quartic kernel. The first coefficient is
normalized to one. The program returns coefficient
estimates, marginal effects, and the derivative of
g evaluated at the index mean. A
bandwidth may be supplied by the user or calculated
using the program's default rule.
Implements multidimensional nonparametric
regression using either local-constant kernel
regression or local linear regression. The program
accepts a user-provided bandwidth or a bandwidth
selected by cross-validation and returns the fitted
value of the outcome at each observation.
Performs linear or nonlinear GMM estimation after
the user supplies the parameters and moment
functions. The code includes examples for a
binary-choice model, a sample-selection model, and
the return-to-schooling application in Lewbel
(2005).
These commands estimate a selected-sample model
with potentially endogenous regressors, given a
very exogenous regressor V in the
selection equation, following Lewbel (2005).
simpsel1 assumes that the
selection-model error is normal.
simpsel2 uses a nonparametric
sorted-data density for the selection-model
error.