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Robust estimation of zero-inflated count models

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Author Info
Kevin E. Staub () (Socioeconomic Institute, University of Zurich)
Rainer Winkelmann () (Socioeconomic Institute, University of Zurich)

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Abstract

Applications of zero-inflated count data models have proliferated in empirical economic research. There is a downside to this development, as zero-inflated Poisson or zero-inflated Negative Binomial Maximum Likelihood estimators are not robust to misspecification. In contrast, simple Poisson regression provides consistent parameter estimates even in the presence of excess zeros. The advantages of the Poisson approach are illustrated in a series of Monte Carlo simulations.

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Publisher Info
Paper provided by University of Zurich, Socioeconomic Institute in its series Working Papers with number 0908.

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Length: 22 pages
Date of creation: Jun 2009
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Handle: RePEc:soz:wpaper:0908

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Related research
Keywords: excess zeros; Poisson; overdispersion; Negative Binomial regression.;

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. List, John A, 2001. "Determinants of Securing Academic Interviews after Tenure Denial: Evidence from a Zero-Inflated Poisson Model," Applied Economics, Taylor and Francis Journals, vol. 33(11), pages 1423-31, September. [Downloadable!] (restricted)
  2. Paula Stephan & Shiferaw Gurmu & Albert Sumell & Grant Black, 2007. "Who'S Patenting In The University? Evidence From The Survey Of Doctorate Recipients," Economics of Innovation and New Technology, Taylor and Francis Journals, vol. 16(2), pages 71-99. [Downloadable!] (restricted)
  3. Victoria Ateca-Amestoy, 2008. "Determining heterogeneous behavior for theater attendance," Journal of Cultural Economics, Springer, vol. 32(2), pages 127-151, June. [Downloadable!] (restricted)
  4. Ken Clark & Simon A. Peters & Mark Tomlinson, 2005. "The Determinants Of Lateness: Evidence From British Workers," Scottish Journal of Political Economy, Scottish Economic Society, vol. 52(2), pages 282-304, 05. [Downloadable!] (restricted)
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  5. Georgios Papadopoulos & J. M. C. Santos Silva, 2008. "Identification issues in models for underreported counts," Economics Discussion Papers 657, University of Essex, Department of Economics. [Downloadable!]
  6. Axel Heitmueller, 2003. "Job Mobility in Britain: Are the Scots Different? Evidence from the BHPS," Working Papers E02, Department of Economics, School of Management and Languages, Heriot Watt University. [Downloadable!]
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Cited by:
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  1. Ilja Neustadt & Peter Zweifel, 2009. "Social Mobility and Preferences for Income Redistribution: Evidence from a Discrete Choice Experiment," Working Papers 0909, University of Zurich, Socioeconomic Institute. [Downloadable!]
  2. Maurus Rischatsch & Maria Trottmann, 2009. "Physician dispensing and the choice between generic and brand-name drugs – Do margins affect choice?," Working Papers 0911, University of Zurich, Socioeconomic Institute. [Downloadable!]
  3. Maurus Rischatsch, 2009. "Simulating WTP Values from Random-Coefficient Models," Working Papers 0912, University of Zurich, Socioeconomic Institute. [Downloadable!]
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