Econometric Analysis of Count DataSpringer Science & Business Media, 2008 M03 7 - 320 páginas The “count data” ?eld has further ?ourished since the previous edition of this book was published in 2003. The development of new methods has not slowed down by any means, and the application of existing ones in applied work has expanded in many areas of social science research. This, in itself, would be reason enough for updating the material in this book, to ensure that it continues to provide a fair representation of the current state of research. In addition, however, I have seized the opportunity to undertake some major changes to the organization of the book itself. The core material on cross-section models for count data is now presented in four chapters, rather than in two as previously. The ?rst of these four chapters introduces the Poissonregressionmodel,anditsestimationbymaximumlikelihoodorpseudo maximum likelihood. The second focuses on unobserved heterogeneity, the third on endogeneity and non-random sample selection. The fourth chapter provides an extended and uni?ed discussion of zeros in count data models. This topic deserves, in my view, special emphasis, as it relates to aspects of modeling and estimation that are speci?c to counts, as opposed to general exponential regression models for non-negative dependent variables. Count distributions put positive probability mass on single o- comes, and thus o?er a richer set of interesting inferences. |
Contenido
1 | |
Poisson Regression | 63 |
Coefficients | 83 |
Unobserved Heterogeneity | 127 |
Sample Selection and Endogeneity | 143 |
Zeros in Count Data Models | 173 |
Correlated Count Data | 203 |
Bayesian Analysis of Count Data | 241 |
Applications | 251 |
A Probability Generating Functions | 281 |
Software 289 | 288 |
299 | |
321 | |
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Términos y frases comunes
additional allows alternative application approach arises assume assumption bivariate Chap chapter conditional consider consistent constant correlation count data models covariance defined denote density dependence derive determined discussed duration effects endogenous equal errors example expected exponential further gamma given gives Hence increase independent individual instance integer interest interpretation introduced job changes joint leads linear log-likelihood marginal matrix maximum likelihood estimator mean function methods mixture multivariate negative binomial negative binomial model Negbin normal distribution Note observed obtained occur outcome overdispersion panel parameter particular Poisson distribution Poisson model Poisson regression model positive possible probability function problem properties provides random variable referred regressors relative requires respect restrictions sample selection specific standard statistic structure tion true truncated unobserved heterogeneity variance Winkelmann zero
Pasajes populares
Página 318 - Winkelmann, R. and KF Zimmermann (1995), Recent Developments in Count Data Modeling: Theory and Applications, Journal of Economic Surveys 9,1- 24.
Página 302 - Medicine 20, 3667-3676. van den Broek, J. (1995). A score test for zero inflation in a Poisson distribution.
Página 299 - Labour participation of Arab women: Estimates of the fertility to labour supply link," Applied Economics 30: 931-941.
Página 299 - The demand for children in Arab countries: Evidence from panel and count data models," Journal of Population Economics 11(3): 435-452.
Referencias a este libro
Univariate Discrete Distributions Norman L. Johnson,Adrienne W. Kemp,Samuel Kotz Vista previa limitada - 2005 |
Multivariate Statistical Modelling Based on Generalized Linear Models Ludwig Fahrmeir,Gerhard Tutz Sin vista previa disponible - 2001 |