Forecast accuracy, coefficient bias and Bayesian vector autoregressions

Ronald Bewley

Mathematics and Computers in Simulation (MATCOM), 2002, vol. 59, issue 1, 163-169

Abstract: A Bayesian vector autoregression (BVAR) can be thought of either as a method of alleviating the burden of the over-parameterisation usually associated with unrestricted VARs, or as a method of correcting coefficient bias when the time series are nonstationary. Monte Carlo evidence is provided to show that the latter appears to be a more important characteristic of BVARs in experiments using a 4-equation cointegrated system, and with that system embedded in a 10-equation model containing six extraneous random walks. It is found that the BVAR model generally performs much better than a VAR in levels and is a viable alternative to a vector error correction model. It is also found that estimating constant terms when there is no drift in the data causes a major deterioration in forecasting performance.

Keywords: VAR; BVAR; Monte Carlo; Time series (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:59:y:2002:i:1:p:163-169

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