Modeling Zero Inflation in Count Data Time Series with Bounded Support


Real count data time series often show an excessive number of zeros, which can form quite different patterns. We develop four extensions of the binomial autoregressive model for autocorrelated counts with a bounded support, which can accommodate a broad variety of zero patterns. The stochastic properties of these models are derived, and ways of parameter estimation and model identification are discussed. The usefulness of the models is illustrated, among others, by an application to the monetary policy decisions of the National Bank of Poland.

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The authors thank the referee for carefully reading the article and for the comments, which greatly improved the article. H.-Y. Kim’s study is supported by the project “Small & Medium Business Administration” under Project S2312692 “Technological Innovation Development Business” for the innovative company in the year 2015. Main parts of this research were completed while H.-Y. Kim stayed as a guest professor at the Helmut Schmidt University in Hamburg.

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Correspondence to Hee-Young Kim.

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Möller, T.A., H. Weiß, C., Kim, HY. et al. Modeling Zero Inflation in Count Data Time Series with Bounded Support. Methodol Comput Appl Probab 20, 589–609 (2018).

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  • Binomial distribution
  • Count data time series
  • Hidden Markov model
  • Markov model
  • Zero inflation

Mathematics Subject Classifications (2010)

  • 62M10
  • 91B70
  • 60G10
  • 60J10