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Treating missing values in INAR(1) models

Andersson, Jonas; Karlis, Dimitris
Working paper
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URI
http://hdl.handle.net/11250/164125
Date
2008-07
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  • Discussion papers (FOR) [531]
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.
Publisher
Norwegian School of Economics and Business Administration. Department of Finance and Management Science
Series
Discussion paper
2008:14

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