The majority of empirical research in economics ignores the
potential benefits of nonparametric methods, while the majority of
advances in nonparametric theory ignores the problems faced in
applied econometrics. This book helps bridge this gap between
applied economists and theoretical nonparametric econometricians.
It discusses in depth, and in terms that someone with only one year
of graduate econometrics can understand, basic to advanced
nonparametric methods. The analysis starts with density estimation
and motivates the procedures through methods that should be
familiar to the reader. It then moves on to kernel regression,
estimation with discrete data, and advanced methods such as
estimation with panel data and instrumental variables models. The
book pays close attention to the issues that arise with
programming, computing speed, and application. In each chapter, the
methods discussed are applied to actual data, paying attention to
presentation of results and potential pitfalls.
General
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