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Forecast covariances in the linear multiregression dynamic model


  • Catriona M. Queen

    (The Open University, Milton Keynes, UK)

  • Ben J. Wright

    (The Open University, Milton Keynes, UK)

  • Casper J. Albers

    (The Open University, Milton Keynes, UK)


The linear multiregression dynamic model (LMDM) is a Bayesian dynamic model which preserves any conditional independence and causal structure across a multivariate time series. The conditional independence structure is used to model the multivariate series by separate (conditional) univariate dynamic linear models, where each series has contemporaneous variables as regressors in its model. Calculating the forecast covariance matrix (which is required for calculating forecast variances in the LMDM) is not always straightforward in its current formulation. In this paper we introduce a simple algebraic form for calculating LMDM forecast covariances. Calculation of the covariance between model regression components can also be useful and we shall present a simple algebraic method for calculating these component covariances. In the LMDM formulation, certain pairs of series are constrained to have zero forecast covariance. We shall also introduce a possible method to relax this restriction. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Catriona M. Queen & Ben J. Wright & Casper J. Albers, 2008. "Forecast covariances in the linear multiregression dynamic model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 175-191.
  • Handle: RePEc:jof:jforec:v:27:y:2008:i:2:p:175-191
    DOI: 10.1002/for.1050

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    References listed on IDEAS

    1. Queen, Catriona M. & Smith, Jim Q. & James, David M., 1994. "Bayesian forecasts in markets with overlapping structures," International Journal of Forecasting, Elsevier, vol. 10(2), pages 209-233, September.
    2. Tebaldi, Claudia & West, Mike & Karr, Alan F, 2002. "Statistical Analyses of Freeway Traffic Flows," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(1), pages 39-68, January.
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