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Count Data Regression Using Series Expansions: With Applications

A. Colin Cameron and Per Johansson
Journal of Applied Econometrics
Vol. 12, No. 3, Special Issue: Econometric Models of Event Counts (May - Jun., 1997), pp. 203-223
Published by: Wiley
Stable URL: http://www.jstor.org/stable/2285246
Page Count: 21
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Count Data Regression Using Series Expansions: With Applications
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Abstract

A new class of parametric regression models for both under- and overdispersed count data is proposed. These models are based on squared polynomial expansions around a Poisson baseline density. The approach is similar to that for continuous data using squared Hermite polynomials proposed by Gallant and Nychka and applied to financial data by, among others, Gallant and Tauchen. The count models are applied to underdispersed data on the number of takeover bids received by targeted firms, and to overdispersed data on the number of visits to health practitioners. The models appear to be particularly useful for underdispersed count data.

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