Abstract #301960

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JSM 2003 Abstract #301960
Activity Number: 85
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301960
Title: The Gibbs Sampler for Detection of Outliers and Structural Shifts with Uninformative Priors for the Shocks Magnitude
Author(s): Teresa Leitao*+
Companies: London School of Economics
Address: Department of Statistics, London, , WC2A 2AE, United Kingdom
Keywords: Gibbs sampler ; structural models ; outliers ; structural shift
Abstract:

This paper presents a Markov chain Monte Carlo method for the estimation of structural time series models, in the presence of interventions, with the prior assumption of a continuous uninformative distribution for the random variable representing the magnitude of the intervention. The type of models considered is a special case of State Space models (SSM). Other existent methods for the estimation of SSMs with interventions in the Bayesian context assume either a discrete or a normal prior for the intervention variables. We propose the use of a uniform prior distribution, which requires less prior knowledge of the time series process. The method proposed is presented for a structural model, with irregular, trend, and seasonal components. We consider four different types of interventions: outliers, level, slope, and seasonal shifts. The Gibbs sampler is used for sampling from the posterior distributions of the variances, magnitude, and probabilities of outliers and structural shifts. The effectiveness of the method is established by a Monte Carlo experiment, and its application to a real dataset.

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