It is rather common to have several competing forecasts for the same variable, and many methods have been suggested to pick up the best, on the basis of their past forecasting performance. As an alternative, the forecasts can be combined to obtain a pooled forecast, and several options are available to select what forecasts should be pooled, and how to determine their relative weights. In this paper we compare the relative performance of alternative pooling methods, using a very large dataset of about 500 macroeconomic variables for the countries in the European Monetary Union. In this case the forecasting exercise is further complicated by the short time span available, due to the need of collecting a homogeneous dataset. For each variable in the dataset, we consider 58 forecasts produced by a range of linear, time-varying and non-linear models, plus 16 pooled forecasts. Our results indicate that on average combination methods work well. Yet, a more disaggregate analysis reveals that single non-linear models can outperform combination forecasts for several series, even though they perform rather badly for other series so that on average their performance is not as good as that of pooled forecasts. Similar results are obtained for a subset of unstable series, the pooled forecasts behave only slightly better, and for three key macroeconomic variables, namely, industrial production, unemployment and inflation.
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Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number
212.
Length: Date of creation: Date of revision: Handle: RePEc:igi:igierp:212
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
David Hendry & Michael P. Clements, 2001.
"Pooling of Forecasts,"
Economics Papers
2002-W9, Economics Group, Nuffield College, University of Oxford.
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David F. Hendry & Michael P. Clements, 2004.
"Pooling of forecasts,"
Econometrics Journal,
Royal Economic Society, vol. 7(1), pages 1-31, 06.
[Downloadable!] (restricted)
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