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The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study

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  • Henzel, Steffen R.
  • Mayr, Johannes

Abstract

This paper analyzes the mechanics of VAR forecast pooling and quantifies the forecast performance under varying conditions. To fill the gap between empirical and purely theoretical research we run a Monte Carlo study and simulate the data from different New Keynesian DSGE models. We find that equally pooling VAR forecasts outperforms single predictions in general and that the gains are substantial for sample sizes relevant in practice. In contrast, the estimation of theoretically optimal weights or model selection is advisable only for very large data sets hardly available in practice. Notably, equally pooling forecasts from small-scale VARs can even dominate forecasts from large VARs including all relevant variables. Given our results, we advocate the use of equally pooled predictions from parsimonious VARs as an easy to implement and competitive forecast approach.

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Bibliographic Info

Article provided by Elsevier in its journal The North American Journal of Economics and Finance.

Volume (Year): 24 (2013)
Issue (Month): C ()
Pages: 1-24

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Handle: RePEc:eee:ecofin:v:24:y:2013:i:c:p:1-24

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Web page: http://www.elsevier.com/locate/inca/620163

Related research

Keywords: Pooling of forecasts; Model uncertainty; VAR model; Monte Carlo study;

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References

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Cited by:
  1. Tim Oliver Berg & Steffen Henzel, 2014. "Point and Density Forecasts for the Euro Area Using Bayesian VARs," CESifo Working Paper Series 4711, CESifo Group Munich.
  2. Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong & Simo-Kengne, Beatrice D., 2014. "Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 170-189.

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