COUNT DATA REGRESSION USING SERIES EXPANSIONS: WITH APPLICATIONS

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 a...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 1997-05, Vol.12 (3), p.203-223
Main Authors: CAMERON, A. COLIN, JOHANSSON, PER
Format: Article
Language:English
Subjects:
Binomials
Data models
Econometrics
Economic models
Health insurance
Modeling
Parametric models
Parsimony
Polynomials
Regression analysis
Series expansion
Statistical methods
Statistical variance
Studies
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Summary: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.
ISSN:0883-7252
1099-1255
1099-1255
DOI:10.1002/(SICI)1099-1255(199705)12:3<203::AID-JAE446>3.0.CO;2-2