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Discrete time-series models when counts are unobservable

  • T M Christensen

    ()

    (QUT)

  • A. S. Hurn

    ()

    (QUT)

  • K A Lindsay

    ()

    (University of Glasgow)

Count data in economics have traditionally been modeled by means of integer-valued autoregressive models. Consequently, the estimation of the parameters of these models and their asymptotic properties have been well documented in the literature. The models comprise a description of the survival of counts generally in terms of a binomial thinning process and an independent arrivals process usually specified in terms of a Poisson distribution. This paper extends the existing class of models to encompass situations in which counts are latent and all that is observed is the presence or absence of counts. This is a potentially important modification as many interesting economic phenomena may have a natural interpretation as a series of 'events' that are driven by an underlying count process which is unobserved. Arrivals of the latent counts are modeled either in terms of the Poisson distribution, where multiple counts may arrive in the sampling interval, or in terms of the Bernoulli distribution, where only one new arrival is allowed in the same sampling interval. The models with latent counts are then applied in two practical illustrations, namely, modeling volatility in financial markets as a function of unobservable 'news' and abnormal price spikes in electricity markets being driven by latent 'stress'.

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File URL: http://www.ncer.edu.au/papers/documents/NCER_WpNo35Sep08.pdf
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Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 35.

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Length: 31
Date of creation: 15 Sep 2008
Date of revision:
Handle: RePEc:qut:auncer:2008-24
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  1. Geman, Hélyette & Roncoroni, Andréa, 2006. "Understanding the Fine Structure of Electricity Prices," Economics Papers from University Paris Dauphine 123456789/1433, Paris Dauphine University.
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  10. Byström, Hans, 2001. "Extreme Value Theory and Extremely Large Electricity Price Changes," Working Papers 2001:19, Lund University, Department of Economics.
  11. McCabe, B.P.M. & Martin, G.M., 2005. "Bayesian predictions of low count time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 315-330.
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  15. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September.
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