Effects of Overdispersion on Testing for Serial Dependence in the Time Series of Counts Data

- Journal title : Communications for Statistical Applications and Methods
- Volume 17, Issue 6, 2010, pp.829-843
- Publisher : The Korean Statistical Society
- DOI : 10.5351/CKSS.2010.17.6.829

Title & Authors

Effects of Overdispersion on Testing for Serial Dependence in the Time Series of Counts Data

Kim, Hee-Young; Park, You-Sung;

Kim, Hee-Young; Park, You-Sung;

Abstract

To test for the serial dependence in time series of counts data, Jung and Tremayne (2003) evaluated the size and power of several tests under the class of INARMA models based on binomial thinning operations for Poisson marginal distributions. The overdispersion phenomenon(i.e., a variance greater than the expectation) is common in the real world. Overdispersed count data can be modeled by using alternative thinning operations such as random coefficient thinning, iterated thinning, and quasi-binomial thinning. Such thinning operations can lead to time series models of counts with negative binomial or generalized Poisson marginal distributions. This paper examines whether the test statistics used by Jung and Tremayne (2003) on serial dependence in time series of counts data are affected by overdispersion.

Keywords

Overdispersion;negative binomial;generalized Poisson;time series of counts data;serial dependence;

Language

English

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