Hermite Regression Analysis of Multi-Modal Count Data
David Giles ()
No 1001, Econometrics Working Papers from Department of Economics, University of Victoria
We discuss the modeling of count data whose empirical distribution is both multi-modal and overdispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximum likelihood, and nests the Poisson model as a special case. The Hermite regression model is applied to data for the number of banking and currency crises in IMF-member countries, and is found to out-perform the Poisson and negative binomial models.
Keywords: Count data; multi-modal data; over-dispersion; financial crises (search for similar items in EconPapers)
JEL-codes: C16 C25 G15 G21 (search for similar items in EconPapers)
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Note: ISSN 1485-6441
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Journal Article: Hermite regression analysis of multi-modal count data (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:1001
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