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An Evaluation of the Forecasting Performance of Three Econometric Models for the Eurozone and the USA

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  • David Mortimer Krainz

Abstract

This paper compares the forecasting performance of three different econometric models for the Eurozone and the USA: a vector auto regression (VAR), a Bayesian vector auto regression (BVAR), and a structural vector error correction model (SVEC). The forecast evaluation is based on 19 vintages of real time data for output, inflation rates, interest rates, the exchange rate and the money stock from the fourth quarter of 2004 until the first quarter of 2010. The oil price is used as the only exogenous variable in the model. Imposing a stringent set of long-run assumptions on the econometric model results in less accurate forecasts. The difference is significant for several variables and forecast horizons. Reducing the comparison to data from the pre-financial crisis period reduces the size of forecast errors but does not change the overall picture.

Suggested Citation

  • David Mortimer Krainz, 2011. "An Evaluation of the Forecasting Performance of Three Econometric Models for the Eurozone and the USA," WIFO Working Papers 399, WIFO.
    • Handle: RePEc:wfo:wpaper:y:2011:i:399
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    1. Paul Gaggl & Serguei Kaniovski & Klaus Prettner & Thomas Url, 2009. "The short and long-run interdependencies between the Eurozone and the USA," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 36(2), pages 209-227, May.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    4. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    5. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    6. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    7. Clements Michael P. & Hendry David F., 2008. "Economic Forecasting in a Changing World," Capitalism and Society, De Gruyter, vol. 3(2), pages 1-20, October.
    8. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
    9. Robert B. Litterman, 1984. "Specifying vector autoregressions for macroeconomic forecasting," Staff Report 92, Federal Reserve Bank of Minneapolis.
    10. J. C. G. Boot & W. Feibes & J. H. C. Lisman, 1967. "Further Methods of Derivation of Quarterly Figures from Annual Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(1), pages 65-75, March.
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    Cited by:

    1. Nicolaas van der Wath, 2016. "Gauging financial conditions in South Africa," Working Papers 10/2016, Stellenbosch University, Department of Economics.
    2. Khaled Guesmi & Nabila BOUKEF JLASSI & Ahmed Atil & Imen Haouet, 2016. "On the Influence of Oil Prices on Financial Variables," Economics Bulletin, AccessEcon, vol. 36(4), pages 2261-2274.
    3. Nicolaas van der Wath, 2013. "Comparing the BER’s forecasts," Working Papers 23/2013, Stellenbosch University, Department of Economics.

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