Estimation of zero-inflated count time series models with and without covariates
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Date
2015
Authors
Ghanney, Bartholomew Embir
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Abstract
Zero inflation occurs when the proportion of zeros of a model is greater than the proportion of zeros of the corresponding Poisson model. This situation is very common in count data. In order to model zero inflated count time series data, we propose the zero inflated autoregressive conditional Poisson (ZIACP) model by the extending the autoregressive conditional poisson (ACP) model of Ghahramani and Thavaneswaran (2009). The stationarity conditions and the autocorrelation functions of the ZIACP model are provided. Based on the expectation maximization (EM) algorithm an estimation method is developed. A simulation study shows that the estimation method is accurate and reliable as long as the sample size is reasonably high. Three real data examples, syphilis data Yang (2012), arson data Zhu (2012) and polio data Kitromilidou and Fokianos (2015) are studied to compare the performance of the proposed model with other competitive models in the literature.
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Keywords
Count data, Zero inflated process, Poisson