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Forecast accuracy, coefficient bias and Bayesian vector autoregressions

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  • Bewley, Ronald

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.

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  • Bewley, Ronald, 2002. "Forecast accuracy, coefficient bias and Bayesian vector autoregressions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(1), pages 163-169.
    • Handle: RePEc:eee:matcom:v:59:y:2002:i:1:p:163-169
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    Cited by:

    1. Günes Kamber & James Morley & Benjamin Wong, 2018. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 550-566, July.
    2. Andrea Bonilla, 2014. "External vulnerabilities and economic integration. Is the Union of South American Nations a promising project ?," Working Papers halshs-00945044, HAL.
    3. repec:rre:publsh:v:40:y:2010:i:2:p:181-96 is not listed on IDEAS
    4. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.

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    Keywords

    VAR; BVAR; Monte Carlo; Time series;
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