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Effects of Overdispersion on Testing for Serial Dependence in the Time Series of Counts Data
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 Title & Authors
Effects of Overdispersion on Testing for Serial Dependence in the Time Series of Counts Data
Kim, Hee-Young; Park, You-Sung;
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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.
Overdispersion;negative binomial;generalized Poisson;time series of counts data;serial dependence;
 Cited by
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