Treating missing values in INAR(1) models

Jonas Andersson and Dimitris Karlis ()
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Dimitris Karlis: Department of Statistics, Athens University of Economics and Business, Postal: Department of Statistics, Athens University of Economics and Business, 76 Patission str, Athens 10434, Greece, http://www.stat-athens.aueb.gr/~karlis/

No 2008/14, Discussion Papers from Norwegian School of Economics, Department of Business and Management Science

Abstract: Time series models for count data have found increased interest in recent days. The existing literature refers to the case of data that have been fully observed. In the present paper, methods for estimating the parameters of the first-order integer-valued autoregressive model in the presence of missing data are proposed. The first method maximizes a conditional likelihood constructed via the observed data based on the k-step-ahead conditional distributions to account for the gaps in the data. The second approach is based on an iterative scheme where missing values are imputed in order to update the estimated parameters. The first method is useful when the predictive distributions have simple forms. We derive in full details this approach when the innovations are assumed to follow a finite mixture of Poisson distributions. The second method is applicable when there are not closed form expressions for the conditional likelihood or they are hard to derive. Simulation results and comparisons of the methods are reported. The proposed methods are applied to a data set concerning syndromic surveillance during the Athens 2004 Olympic Games.

Keywords: Imputation; Markov Chain EM algorithm; mixed Poisson; discrete valued time series (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2008-08-13
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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