Statistical Modelling 2 (2002), 322
Semiparametric methods in applied econometrics: do the models fit the data?
Joel L Horowitz
Department of Economics, Northwestern University,
2003 Sheridan Road
Evanston, IL 60208-2600
USA
Sokbae Lee,
Department of Economics, University of Iowa,
USA
Abstract:
Much empirical
research in economics and other fields is concerned with estimating the mean of
a random variable conditional on one or more explanatory variables (conditional
mean function). The most frequently used estimation methods assume that the
conditional mean function is known up to a finite number of parameters, but the
resulting estimates can be highly misleading, if the assumed parametric model
is incorrect. This paper reviews several semiparametric methods for estimating
conditional mean functions. These methods are more flexible than parametric
methods and offer greater estimation precision than do fully nonparametric
methods. The various estimation methods are illustrated by applying them to
data on the salaries of professional baseball players in the USA. We find that
a parametric models and several simple semiparametric models fail to capture
important features of the data. However, a sufficiently rich semiparametric
model fits the data well. We conclude that semiparametric models can achieve
their aim of providing flexible representations of conditional mean functions,
but care is needed in choosing the semiparametric specification. Our analysis
also provides some suggestions for further research on semiparametric
estimation.
Keywords:
Additive model; Dimension reduction; Index model;
Nonparametric regression.
Downloads:
Data and software availabe from:
http://www.faculty.econ.northwestern.edu/faculty/horowitz/papers
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