Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami

Por: Colaborador(es): Idioma: Inglés Detalles de publicación: New York Cambridge University Press 2015Descripción: xii, 367 páginas gráficasISBN:
  • 9781107010253
  • 9780521279680
Tema(s): Clasificación LoC:
  • HB139 .H453 2015
Contenidos:
Machine generated contents note: 1. Introduction; 2. Univariate density estimation; 3. Multivariate density estimation; 4. Testing; 5. Regression; 6. Testing; 7. Smoothing discrete variables; 8. Regression with discrete covariates; 9. Semiparametric methods; 10. Instrumental variables; 11. Panel data; 12. Constrained estimation and inference.
Resumen: "Bridging the gap between applied economists and theoretical nonparametric econometricians, this book explains basic to advanced nonparametric methods with applications"--Resumen: "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"--
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Machine generated contents note: 1. Introduction; 2. Univariate density estimation; 3. Multivariate density estimation; 4. Testing; 5. Regression; 6. Testing; 7. Smoothing discrete variables; 8. Regression with discrete covariates; 9. Semiparametric methods; 10. Instrumental variables; 11. Panel data; 12. Constrained estimation and inference.

"Bridging the gap between applied economists and theoretical nonparametric econometricians, this book explains basic to advanced nonparametric methods with applications"--

"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"--

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