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

Author

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  • William W. Chow

    (Center for Economic Development, Hong Kong University of Science and Technology, Kowloon, Hong Kong)

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.

Suggested Citation

  • William W. Chow, 2004. "An outlier robust hierarchical Bayes model for forecasting: the case of Hong Kong," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 99-114.
    • Handle: RePEc:jof:jforec:v:23:y:2004:i:2:p:99-114
    DOI: 10.1002/for.900
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    References listed on IDEAS

    as
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    Cited by:

    1. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.

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