Conditional forecasts in dynamic multivariate models
AbstractIn the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for the parameter uncertainty in small samples. Empirical examples under the flat prior and under the reference prior of Sims and Zha (1998) are provided to show the use of these methods.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Atlanta in its series Working Paper with number 98-22.
Date of creation: 1998
Date of revision:
Other versions of this item:
- Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
- NEP-ALL-1999-01-25 (All new papers)
- NEP-ECM-1999-01-25 (Econometrics)
- NEP-ETS-1999-01-25 (Econometric Time Series)
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Blog mentionsAs found by EconAcademics.org, the blog aggregator for Economics research:
- Prior elicitation in dynamic models
by Andrew in Statistical Modeling, Causal Inference, and Social Science on 2008-09-06 16:08:00
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