Implementing bayesian vector autoregressions

• Richard M. Todd ^[1]
  1. [1] Federal Reserve Bank of Minneapolis
• Localización: Revista de análisis económico, ISSN-e 0718-8870, ISSN 0716-5927, Vol. 3, Nº 2, 1988, págs. 21-44
• Idioma: inglés
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• Resumen

  • This paper discusses how the Bayesian approach can be used to construct a type of multivariate forecasting model known as a Bayesian vector autoregression (BVAR). In doing so, we mainly explain Doan, Littermann, and Sims (1984) propositions on how to estimate a BVAR based on a certain family of prior probability distributions. indexed by a fairly small set of hyperparameters. There is also a discussion on how to specify a BVAR and set up a BVAR database. A 4-variable model is used to iliustrate the BVAR approach.