Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributionsof unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and 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, and they are being used in appliedresearch with increasing frequency. This second edition has over 100 pages ofnew material INDICE: Introduction.- Single index models.- Nonparametric additive modelsand semiparametric partially linear models.- Binary response models.- Statistical inverse problems.- Transformation models.- Appendix: Nonparametric density estimation and nonparametric regression.
- ISBN: 978-0-387-92869-2
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 285
- Fecha Publicación: 01/08/2009
- Nº Volúmenes: 1
- Idioma: Inglés