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Bayesian methods for dynamic multivariate models

  • Christopher A. Sims
  • Tao Zha

If multivariate dynamic models are to be used to guide decision-making, it is important that it be possible to provide probability assessments of their results. Bayesian VAR models in the existing literature have not commonly (in fact, not at all as far as we know) been presented with error bands around forecasts or policy projections based on the posterior distribution. In this paper we show that it is possible to introduce prior information in both reduced form and structural VAR models without introducing substantial new computational burdens. With our approach, identified VAR analysis of large systems (e.g., 20-variable models) becomes possible.

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Paper provided by Federal Reserve Bank of Atlanta in its series Working Paper with number 96-13.

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Date of creation: 1996
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Handle: RePEc:fip:fedawp:96-13
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