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dc.contributor.authorAndersson, Jonas
dc.contributor.authorKarlis, Dimitris
dc.date.accessioned2008-10-21T10:38:38Z
dc.date.available2008-10-21T10:38:38Z
dc.date.issued2008-07
dc.identifier.issn1500-4066
dc.identifier.urihttp://hdl.handle.net/11250/164125
dc.description.abstractTime 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.en
dc.language.isoengen
dc.publisherNorwegian School of Economics and Business Administration. Department of Finance and Management Scienceen
dc.relation.ispartofseriesDiscussion paperen
dc.relation.ispartofseries2008:14en
dc.subjectimputationen
dc.subjectMarkov Chain EM algorithmen
dc.subjectmixed poissonen
dc.subjectdiscrete valued time seriesen
dc.titleTreating missing values in INAR(1) modelsen
dc.typeWorking paperen
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210en
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410en


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