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Modeling Count Data with Excess Zeroes

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  • Hoong Chor Chin
  • Mohammed Abdul Quddus

Abstract

There are many studies in social sciences, such as traffic accident analysis, in which the event counts may be characterized by a large number of zero observations. In this article, a proposed model that takes into account both the zero-count state and the nonzero-count state is used to describe the traffic accident phenomenon. The probability of the zero-count state (p) and the mean number of event counts (µ) in the non-zero-count state may depend on the covariates. Sometimes, p and µ are unrelated, while at other times, p may assume a simple function of µ.In proposing the model, different types of traffic accidents at signalized intersections in Singapore were investigated. The results demonstrate that the zero-altered probability process is an appropriate technique for modeling specific types of accidents in which the data contain many zero counts.

Suggested Citation

  • Hoong Chor Chin & Mohammed Abdul Quddus, 2003. "Modeling Count Data with Excess Zeroes," Sociological Methods & Research, , vol. 32(1), pages 90-116, August.
  • Handle: RePEc:sae:somere:v:32:y:2003:i:1:p:90-116
    DOI: 10.1177/0049124103253459
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    1. Cameron, A Colin & Trivedi, Pravin K, 1986. "Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
    2. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    4. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    5. Gurmu, Shiferaw & Trivedi, Pravin K, 1996. "Excess Zeros in Count Models for Recreational Trips," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 469-477, October.
    6. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    7. Winfried Pohlmeier & Volker Ulrich, 1995. "An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care," Journal of Human Resources, University of Wisconsin Press, vol. 30(2), pages 339-361.
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