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Hermite Regression Analysis of Multi-Modal Count Data

David Giles ()

No 1001, Econometrics Working Papers from Department of Economics, University of Victoria

Abstract: 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)
New Economics Papers: this item is included in nep-ecm
Date: 2010-04-13
Note: ISSN 1485-6441
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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:1001

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