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Useful models for time series of counts or simply wrong ones?

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Abstract

There has been a considerable and growing interest in low integer-valued time series data leading to a diversification of modelling approaches. In addition to static regression models, both observation-driven and parameter-driven models are considered here. We compare and contrast a variety of time series models for counts using two very different data sets as a testbed. A range of diagnostic devices is employed to help inform model adequacy. Special attention is paid to dynamic structure and underlying distributional assumptions including associated dispersion properties. Competing models show attractive features, but overall no one modelling approach is seen to dominate.

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Authors and Affiliations

  1. Staatswissenschaftliche Fakultät, Universität Erfurt, Nordhäuser Str. 63, 99089, Erfurt, Germany

    Robert C. Jung

  2. School of Economics, University of New South Wales, Sydney, Australia

    A. R. Tremayne

  3. University of York, York, UK

    A. R. Tremayne

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  1. Robert C. Jung
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  2. A. R. Tremayne
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Correspondence to Robert C. Jung.

Additional information

The original seeds of this paper can be found in an invited lecture given by the first author at the Pfingsttagung of the DStatG in Hamburg 2006. We are grateful to the DFG for financial support under the grant JU 2776/2-1.

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Jung, R.C., Tremayne, A.R. Useful models for time series of counts or simply wrong ones?. AStA Adv Stat Anal 95, 59–91 (2011). https://doi.org/10.1007/s10182-010-0139-9

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  • DOI: https://doi.org/10.1007/s10182-010-0139-9

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