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Analysis of low count time series data by poisson autoregression

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Author Info
R. K. Freeland
B. P. M. McCabe
Abstract

This study provides new methods of assessing the adequacy of the Poisson autoregressive time-series model for count data. New expressions are given for the score function and the information matrix and these lead to the construction of new types of residuals for this model. However, these residuals often need to be supplemented by formal statistical procedures and an overall test of the model adequacy is given via the information matrix equality that holds for correctly specified models. The techniques are applied to a monthly count data set of claimants for wage loss benefit, in order to estimate the the expected duration of claimants in the system. Copyright 2004 Blackwell Publishing Ltd.

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Article provided by Blackwell Publishing in its journal Journal of Time Series Analysis.

Volume (Year): 25 (2004)
Issue (Month): 5 (09)
Pages: 701-722
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Handle: RePEc:bla:jtsera:v:25:y:2004:i:5:p:701-722

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  1. T M Christensen & A. S. Hurn & K A Lindsay, 2008. "Discrete time-series models when counts are unobservable," NCER Working Paper Series 35, National Centre for Econometric Research. [Downloadable!]
  2. Christian Weiß, 2008. "Thinning operations for modeling time series of counts—a survey," AStA Advances in Statistical Analysis, Springer, vol. 92(3), pages 319-341, August. [Downloadable!] (restricted)
  3. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  4. T M Christensen & A S Hurn & K A Lindsay, 2008. "It never rains but it pours: Modelling the persistence of spikes in electricity prices," NCER Working Paper Series 25, National Centre for Econometric Research. [Downloadable!]
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This page was last updated on 2009-11-22.


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