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The Virtues of VAR Forecast Pooling - A DSGE Model Based Monte Carlo Study


  • Steffen Henzel
  • Johannes Mayr


Since the seminal article of Bates and Granger (1969), a large number of theoretical and empirical studies have shown that pooling different forecasts of the same event tends to outperform individual forecasts in terms of forecast accuracy. However, the results remain heterogenous regarding the size of gains. As there are numerous sources for the large variation of the resulting gains, it is difficult to estimate the improvement in accuracy based on empirical findings. Consequently, we use Monte Carlo techniques which enable us to identify the gains of pooling from VAR forecasts under lab conditions. In particular, the results are allowed to vary with respect to sample size, forecast horizon, number of pooled forecasts, weighting scheme and structure of the model economy. Given strict lab conditions, our setup of the experiment yields a quantification of the virtues that can be obtained in almost any forecast situation. The analysis shows that pooling leads to a substantial reduction of MSE of about 20%, which is comparable to the elimination of estimation uncertainty. Most notably, this reduction is already obtained with an average of about four different forecasts.

Suggested Citation

  • Steffen Henzel & Johannes Mayr, 2009. "The Virtues of VAR Forecast Pooling - A DSGE Model Based Monte Carlo Study," ifo Working Paper Series 65, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  • Handle: RePEc:ces:ifowps:_65

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    References listed on IDEAS

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    Cited by:

    1. Christian Grimme & Steffen Henzel & Elisabeth Wieland, 2014. "Inflation uncertainty revisited: a proposal for robust measurement," Empirical Economics, Springer, vol. 47(4), pages 1497-1523, December.
    2. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    3. Robert Lehmann & Wolfgang Nierhaus & Magnus Reif, 2016. "Eine Flash-Schätzung für die privaten Konsumausgaben in Deutschland," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(21), pages 36-41, November.
    4. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    5. Anna Billharz & Steffen Elstner & Marcus Jüppner, 2012. "Methoden der ifo Kurzfristprognose am Beispiel der Ausrüstungsinvestitionen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(21), pages 24-33, November.
    6. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    7. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.

    More about this item


    Pooling of forecasts; model uncertainty; VAR model; Monte Carlo Study;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications


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