Semiparametric Methods in Econometrics
Опубликовано на портале: 31-08-2003
New York: Springer-Verlag, 2003
Standard methods for estimating empirical models in economics rely heavily on assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions greatly simplify estimation and statistical inference bu are rarely justified by economic theory or other a priori assumptions. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly erroneous. Semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields.
In recent years, semiparametric estimation problems have generated a large literature in econometrics and statistics. Most of this literature is highly technical, and much is divorced from applications. This book presents the main ideas underlying a variety of semiparametric methods in a way that will be accessible to graduate students and applied researchers who are familiar with econometric theory at the level taught in graduate-level courses. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. There are empirical examples that illustrate the methods that are presented and examples without data of applied problems in which semiparametric methods can be useful.
2. Single index models
3. Binary response models
4. Deconvolution problems
5. Transformation models
Appendix: Nonparametric Estimation