Discrete time-series models when counts are unobservable

T M Christensen (), Stan Hurn and K A Lindsay ()
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T M Christensen: QUT
K A Lindsay: University of Glasgow

No 35, NCER Working Paper Series from National Centre for Econometric Research

Abstract: 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'.

Keywords: Integer-valued autoregression; Poisson distribution; Bernoulli distribution; latent factors; maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C13 C25 C32 (search for similar items in EconPapers)
Pages: 31
Date: 2008-09-15
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:qut:auncer:2008-24

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