Modeling zero inflation in count data time series with bounded support
(Tobias A. Möller, Christian H. Weiß, Hee-Young Kim, and Andrei Sirchenko)
Forthcoming in Methodology and Computing in Applied Probability
ABSTRACT: 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.
ABSTRACT: 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.
KEYWORDS: binomial distribution · count data time series · Hidden Markov model · Markov model · zero inflation
KEYWORDS: binomial distribution · count data time series · Hidden Markov model · Markov model · zero inflation
MATHEMATICS SUBJECT CLASSIFICATION (2000): 62M10 · 91B70 · 60G10 · 60J10
MATHEMATICS SUBJECT CLASSIFICATION (2000): 62M10 · 91B70 · 60G10 · 60J10