Constructing stationary time series models using auxiliary variables with applications

Pitt, M.K. and Walker, S.G. (2005) Constructing stationary time series models using auxiliary variables with applications. Journal of the American Statistical Association, 100 (470). pp. 554-564. ISSN 0162-1459. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1198/016214504000001970

Abstract

Here we present a novel method for modeling stationary time series. Our approach is to construct the model with a specified marginal family and build the dependence structure around it. We show that the resulting time series is linear with a simple autocorrelation structure. We construct models that parallel existing structures, namely state-space models, autoregressive conditional heteroscedasticity (ARCH) models, and generalized ARCH models. We use Bayesian techniques to estimate the resulting models. We also demonstrate that the models perform well compared with competing methods for the applications considered, count models and volatility models.

Item Type: Article
Uncontrolled keywords: autoregressive conditional heteroscedasticity; exponential family; filtering; generalized autoregressive conditional heteroscedasticity; Gibbs sampler; Markov chain; Markov chain Monte Carlo; stochastic volatility distributions; likelihood; families; margins; prices
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Judith Broom
Date Deposited: 11 Sep 2008 15:57
Last Modified: 14 Jan 2010 14:40
Resource URI: http://kar.kent.ac.uk/id/eprint/10542 (The current URI for this page, for reference purposes)
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