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.
|Publisher:||Cambridge University Press|
|Product dimensions:||7.01(w) x 9.88(h) x 1.10(d)|
About the Author
Daniel J. Henderson is the J. Weldon and Delores Cole Faculty Fellow at the University of Alabama, as well as a research fellow at the Institute for the Study of Labor (IZA) in Bonn, Germany, and at the Wang Yanan Institute for Studies in Economics, Xiamen University, in Xiamen, China. He was formerly an associate and Assistant Professor of Economics at the State University of New York at Binghamton. He has held visiting appointments at the Institute of Statistics, Université catholique de Louvain, in Louvain-la-Neuve, Belgium, and in the Department of Economics at Southern Methodist University in Dallas, Texas. He received his PhD in economics from the University of California, Riverside. His work has been published in journals such as the Economic Journal, the European Economic Review, the International Economic Review, the Journal of Applied Econometrics, the Journal of Econometrics, the Journal of Human Resources, the Journal of the Royal Statistical Society, and the Review of Economics and Statistics.
Christopher F. Parmeter is an Associate Professor at the University of Miami. He was formerly an Assistant Professor in the Department of Agricultural and Applied Economics at Virginia Polytechnic Institute and State University. He was also a visiting scholar in Dipartimento di Studi su Politica Diritto e Societa at the University of Palermo. He received his PhD in economics from the State University of New York, Binghamton. His research focuses on applied econometrics across a broad array of fields in economics, including economic growth, microfinance, international trade, environmental economics, and health economics. His work has been published in journals such as the Economic Journal, the European Economic Review, Health Economics, the Journal of Applied Econometrics, the Journal of Econometrics, the Journal of Environmental Economics and Management, and Statistica Sinica.
Table of Contents
1. Introduction; 2. Univariate density estimation; 3. Multivariate density estimation; 4. Inference about the density; 5. Regression; 6. Testing in regression; 7. Smoothing discrete variables; 8. Regression with discrete covariates; 9. Semiparametric methods; 10. Instrumental variables; 11. Panel data; 12. Constrained estimation and inference; Bibliography; Index.