Micro-econometrics
methodos of moments and limited dependent variables
- ISBN: 9780387953762
- Editorial: Springer International Publishing AG
- Fecha de la edición: 2009
- Lugar de la edición: Heidelberg. Alemania
- Edición número: 2nd ed
- Encuadernación: Cartoné
- Medidas: 24 cm
- Nº Pág.: 769
- Idiomas: Inglés
This book introduces econometrics at the graduate level, and then specializes in micro-econometrics topics such as method of moments, limited and qualitative dependent variables, sample-selection models, panel data, nonparametric estimators and specification tests, and semi (non)-parametric methods. The coverage is up-to-date and broad as well as in depth. Many empirical examples are included along with a computer program appendix. Both graduate students and researchers, applied or theoretical, in all disciplines using observational data will find this book useful as a textbook as well as a research monograph for self-study and reference. The second edition is three times length of the first edition One chapter on liner equation systems has been added and several new sections on panel data are new. Also sections for the following topics have been added: LDV's with endogenous regressors, competing risks, nonparametric survival and hazard function estimation, rank-based semiparametric methods, differencing-based semiparametric methods, semiparametric estimators for duration models, integrated moment specification tests, nonparametric control function approaches, nonparametric additive models, various transformation of response variables, and nonparametric specification and significance tests. The appendix now contains the proofs for some important results in the main text and new sections for the following topics: review of mathematical and statistical backgrounds, nested logit, U-statistics, GMM with integrated squared moments, goodness-of-fit tests for distribution functions, joint test for all quantiles, review on test, non-nested model test, stratified sampling and weighted M-estimator, empirical likelihood estimator, stochastic-process convergence and applications, and bootstrap.