Smoothing Choices and Distributional
Approximations for Econometric Inference
(Ph.D. dissertation,
University of California, Berkeley, 2004)
Abstract
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This dissertation treats a number of related issues involved in the
practical implementation of some popular semiparametric estimation techniques
in econometrics. It is generally
recognized that many problems of statistical inference in economics have a
semiparametric form in the sense that they are characterized by the presence
of both unknown finite-dimensional parameters as well as unknown
functions. In most cases estimators
for the finite-dimensional parameter in semiparametric models are
well-established and can be shown to be root-n-consistent and asymptotically
normal, with asymptotic covariance matrices attaining a semiparametric
efficiency bound. In this dissertation
it is argued that the problems of estimation of the unknown functions and
approximation of the actual sampling variability of estimators of the finite-dimensional
parameters are both important and interrelated, particularly when considering
how to reduce discrepancies between the actual and nominal rejection
probabilities of associated hypothesis tests.
One of the chapters that follow presents an approach to the problem of
implementing estimators of unknown functions in a popular class of
semiparametric regression estimator.
This is followed by a presentation of a method of size correction for
tests based on regression quantile estimators as developed by Koenker and
Bassett (1978). |
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