Implementing Bayesian Vector Autoregressions

Authors

  • Richard M. Todd Research Department, Federal Reserve Bank of Minneapolis image/svg+xml

Keywords:

Bayesian vector autoregression, BVAR, multivariate forecasting, prior distributions, hyperparameters, econometric models

Abstract

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, Litterman, 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 illustrate the BVAR approach.

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References

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Published

1988-10-31

How to Cite

Todd, R. M. (1988). Implementing Bayesian Vector Autoregressions. Economic Analysis Review, 3(2), 21–44. Retrieved from https://www.rae-ear.org/index.php/rae/article/view/284

Issue

Vol. 3 No. 2 (1988)

Section

Articles

License

Copyright (c) 1988 Richard M. Todd

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