An outlier robust hierarchical Bayes model for forecasting: the case of Hong Kong

William W. Chow
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William W. Chow: Center for Economic Development, Hong Kong University of Science and Technology, Kowloon, Hong Kong, Postal: Center for Economic Development, Hong Kong University of Science and Technology, Kowloon, Hong Kong

Journal of Forecasting, 2004, vol. 23, issue 2, 99-114

Abstract: This paper introduces a Bayesian forecasting model that accommodates innovative outliers. The hierarchical specification of prior distributions allows an identification of observations contaminated by these outliers and endogenously determines the hyperparameters of the Minnesota prior. Estimation and prediction are performed using Markov chain Monte Carlo (MCMC) methods. The model forecasts the Hong Kong economy more accurately than the standard V AR and performs in line with other complicated BV AR models. It is also shown that the model is capable of finding most of the outliers in various simulation experiments. Copyright © 2004 John Wiley & Sons, Ltd.

Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:23:y:2004:i:2:p:99-114

DOI: 10.1002/for.900

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