Applied Nonparametric Econometrics
Business & Finance, Economics, Econometrics, Nonfiction, Science & Nature, Mathematics
| Author: | Daniel J. Henderson, Christopher F. Parmeter | ISBN: | 9781316053560 |
| Publisher: | Cambridge University Press | Publication: | January 12, 2015 |
| Imprint: | Cambridge University Press | Language: | English |
| Author: | Daniel J. Henderson, Christopher F. Parmeter |
| ISBN: | 9781316053560 |
| Publisher: | Cambridge University Press |
| Publication: | January 12, 2015 |
| Imprint: | Cambridge University Press |
| Language: | English |
The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore 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.
The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore 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.
