The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics
Oxford University Press Inc (Verlag)
978-0-19-985794-4 (ISBN)
This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. These data-driven models seek to replace the "classical " parametric models of the past, which were rigid and often linear. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures. They provide a balanced view of new developments in the analysis and modeling of applied sciences with cross-section, time series, panel, and spatial data sets. The major topics of the volume include: the methodology of semiparametric models and special regressor methods; inverse, ill-posed, and well-posed problems; different methodologies related to additive models; sieve regression estimators, nonparametric and semiparametric regression models, and the true error of competing approximate models; support vector machines and their modeling of default probability; series estimation of stochastic processes and some of their applications in Econometrics; identification, estimation, and specification problems in a class of semilinear time series models; nonparametric and semiparametric techniques applied to nonstationary or near nonstationary variables; the estimation of a set of regression equations; and a new approach to the analysis of nonparametric models with exogenous treatment assignment.
Jeffrey Racine is Professor in the Department of Economics and the Department of Mathematics and Statistics as well as Senator William McMaster Chair in Econometrics at McMaster University. Liangjun Su is Associate Professor in the School of Economics at Singapore Management University and in the Guanghua School of Management at Peking University. Aman Ullah is Distinguished Professor in the Department of Economics at University of California, Riverside.
Contents ; List of Contributors ; Preface ; PART 1: METHODOLOGY ; 1. The Hilbert Space Theoretical Foundation of Semi-Nonparametric Modeling ; Herman J. Bierens ; 2. An Overview of the Special Regressor Method ; Arthur Lewbel ; PART 2: INVERSE PROBLEMS ; 3. Asymptotic Normal Inference in Linear Inverse Problems ; Marine Carrasco, Jean-Pierre Florens, and Eric Renault ; 4. Identification and Well-Posedness in Nonparametric Models with Independence Conditions ; Victoria Zinde-Walsh ; PART 3: ADDITIVE MODELS ; 5. Nonparametric Additive Models ; Joel L. Horowitz ; 6. Oracally Efficient Two-Step Estimation for Additive Regression ; Shujie Ma and Lijian Yang ; 7. Additive Models: Extensions and Related Models ; Enno Mammen, Byeong U. Park, and Melanie Schienle ; PART 4: MODEL SELECTION AND AVERAGING ; 8. Nonparametric Sieve Regression: Least Squares, Averaging Least Squares, and Cross-Validation ; Bruce E. Hansen ; 9. Variable Selection in Nonparametric and Semiparametric Regression Models ; Liangjun Su and Yonghui Zhang ; 10. Data-Driven Model Evaluation: A Test for Revealed Performance ; Jeffrey S. Racine and Christopher F. Parmeter ; 11. Support Vector Machines with Evolutionary Model Selection for Default Prediction ; Wolfgang Karl Hardle, Dedy Dwi Prastyo, and Christian Hafner ; PART 5: TIME SERIES ; 12. Series Estimation of Stochastic Processes: Recent Developments and Econometric Applications ; Peter C.B. Phillips and Zhipeng Liao ; 13. Identification, Estimation, and Specification in a Class of Semi-Linear Time Series Models ; Jiti Gao ; 14. Nonparametric and Semiparametric Estimation and Hypothesis Testing with Nonstationary Time Series ; Yiguo Sun and Qi Li ; PART 6: CROSS SECTION ; 15. Nonparametric and Semiparametric Estimation of a Set of Regression Equations ; Aman Ullah and Yun Wang ; 16. Searching for Rehabilitation in Nonparametric Regression Models with Exogenous Treatment Assignment ; Daniel J. Henderson and Esfandiar Maasoumi
| Reihe/Serie | Oxford Handbooks |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 180 x 249 mm |
| Gewicht | 1043 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 0-19-985794-6 / 0199857946 |
| ISBN-13 | 978-0-19-985794-4 / 9780199857944 |
| Zustand | Neuware |
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