| Document Type
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BL
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| Record Number
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949114
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| Doc. No
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b703484
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| Main Entry
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Greene, William H.,1951-
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| Title & Author
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Functional form and heterogeneity in models for count data /\ William Greene.
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| Publication Statement
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Boston :: Now,, ©2007.
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| Series Statement
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Foundations and trends in econometrics ;; v. 1, no. 2
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| Page. NO
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1 online resource (ix, 110 pages) :: color illustrations
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| ISBN
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1601980558
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: 9781601980557
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9781601980540
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| Notes
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Offprint. Foundations and trends in econometrics (Online). Vol. 1, no. 2 (2005).
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Title from PDF title page (viewed Jan. 28, 2008).
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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Abstract -- 1. Introduction -- 2. Basic functional forms for count data models -- 3. Two part models -- 4. Models for panel data -- 5. The bivariate Poisson model -- 6. Applications -- 7. Conclusions -- Appendix A: Log likelihood and gradient for NBP model -- Appendix B: Derivatives of partial effects in the Poisson model with sample selection -- Appendix C: Derivatives of partial effects of ZIP model with endogenous zero inflation -- Appendix D: Derivatives of partial effects in hurdle models -- Appendix E: Partial effects and derivatives of partial effects in hurdle models with endogenous participation -- References -- Updates.
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| Abstract
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This study presents several extensions of the most familiar models for count data, the Poisson and negative binomial models. We develop an encompassing model for two well-known variants of the negative binomial model (the NB1 and NB2 forms). We then analyze some alternative approaches to the standard log gamma model for introducing heterogeneity into the loglinear conditional means for these models. The lognormal model provides a versatile alternative specification that is more flexible (and more natural) than the log gamma form, and provides a platform for several "two part" extensions, including zero inflation, hurdle, and sample selection models. (We briefly present some alternative approaches to modeling heterogeneity.) We also resolve some features in Hausman, Hall and Griliches (1984, Economic models for count data with an application to the patents-R&D relationship, Econometrica 52, 909-938) widely used panel data treatments for the Poisson and negative binomial models that appear to conflict with more familiar models of fixed and random effects. Finally, we consider a bivariate Poisson model that is also based on the lognormal heterogeneity model. Two recent applications have used this model. We suggest that the correlation estimated in their model frameworks is an ambiguous measure of the correlation of the variables of interest, and may substantially overstate it. We conclude with a detailed application of the proposed methods using the data employed in one of the two aforementioned bivariate Poisson studies.
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| Subject
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Negative binomial distribution-- Mathematical models.
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Poisson distribution-- Mathematical models.
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Regression analysis-- Mathematical models.
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MATHEMATICS-- Probability Statistics-- General.
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Poisson distribution-- Mathematical models.
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Regression analysis-- Mathematical models.
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| Dewey Classification
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519.2/4
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| LC Classification
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QA273.6.G74 2007eb
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