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Modelling and coherent forecasting of zero-inflated time series count data

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  • Maiti, Raju
  • Biswas, Atanu
  • Guha, Apratim
  • Seng, Huat Ong

Abstract

In this article, a new kind of stationary zero-inflated Pegram's operator based integer-valued time series process of order p with Poisson marginal or ZIPPAR(p) is constructed for modelling a count time series consisting a large number of zeros compared to standard Poisson time series processes. Estimates of the model parameters are studied using three methods, namely Yule-Walker, conditional least squares and maximum likelihood estimation. Also h-step ahead coherent forecasting distributions of the proposed process for p = 1; 2 are derived. Real data set is used to examine and illustrate the proposed model with some simulation studies.

Suggested Citation

  • Maiti, Raju & Biswas, Atanu & Guha, Apratim & Seng, Huat Ong, 2013. "Modelling and coherent forecasting of zero-inflated time series count data," IIMA Working Papers WP2013-05-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    • Handle: RePEc:iim:iimawp:12103
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    Cited by:

    1. Atanu Biswas & Maria Carmen Pardo & Apratim Guha, 2014. "Auto-association measures for stationary time series of categorical data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 487-514, September.

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