The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics

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. Contributors to this volume - Herman J. Bierens Marine Carrasco Jean-Pierre Florens Jiti Gao Christian Hafner Bruce E. Hansen Wolfgang Karl Hardle Daniel J. Henderson Joel L. Horowitz Arthur Lewbel Qi Li Zhipeng Liao Shujie Ma Esfandiar Maasoumi Enno Mammen Byeong U. Park Christopher F. Parmeter Peter C. B. Phillips Dedy Dwi Prastyo Jeffrey S. Racine Eric Renault Melanie Schienle Liangjun Su Yiguo Sun Aman Ullah Yun Wang Lijian Yang Yonghui Zhang Victoria Zinde-Walsh