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Analysis of low count time series data by poisson autoregression

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  • R. K. Freeland
  • B. P. M. McCabe

Abstract

. This study provides new methods of assessing the adequacy of the Poisson autoregressive time‐series model for count data. New expressions are given for the score function and the information matrix and these lead to the construction of new types of residuals for this model. However, these residuals often need to be supplemented by formal statistical procedures and an overall test of the model adequacy is given via the information matrix equality that holds for correctly specified models. The techniques are applied to a monthly count data set of claimants for wage loss benefit, in order to estimate the the expected duration of claimants in the system.

Suggested Citation

  • R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, September.
    • Handle: RePEc:bla:jtsera:v:25:y:2004:i:5:p:701-722
    DOI: 10.1111/j.1467-9892.2004.01885.x
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    References listed on IDEAS

    as
    1. Robert C. Jung & A. R. Tremayne, 2003. "Testing for serial dependence in time series models of counts," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 65-84, January.
    2. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    3. Du Jin‐Guan & Li Yuan, 1991. "THE INTEGER‐VALUED AUTOREGRESSIVE (INAR(p)) MODEL," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(2), pages 129-142, March.
    4. Chesher, Andrew D, 1984. "Testing for Neglected Heterogeneity," Econometrica, Econometric Society, vol. 52(4), pages 865-872, July.
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