Blockwise empirical likelihood for time series of counts

Time series of counts has a wide variety of applications in real life. Analyzing time series of counts requires accommodations for serial dependence, discreteness, and overdispersion of data. In this paper, we extend blockwise empirical likelihood (Kitamura, 1997) to the analysis of time series of counts under a regression setting. In particular, our contribution is the extension of Kitamura’s (1997) method to the analysis of nonstationary time series. Serial dependence among observations is treated nonparametrically using blocking technique; and overdispersion in count data is accommodated by the specification of variance-mean relationship. We establish consistency and asymptotic normality of the maximum blockwise empirical likelihood estimator. Simulation studies show that our method has a good finite sample performance. The method is also illustrated by analyzing two real data sets: monthly counts of poliomyelitis cases in the U.S.A. and daily counts of non-accidental deaths in Toronto, Canada.