Forecast Pooling for European Macroeconomic Variables

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.

[1]  A. Silvestrini,et al.  Using intra annual information to forecast the annual state deficits. The case of France , 2004 .

[2]  J. Stock,et al.  Macroeconomic forecasting in the Euro area: Country specific versus area-wide information , 2003 .

[3]  M. Marcellino,et al.  Instability and Non-Linearity in the Emu , 2002 .

[4]  Michael P. Clements,et al.  Pooling of Forecasts , 2004 .

[5]  Massimiliano Marcellino,et al.  Fiscal forecasting: The track record of the IMF, OECD and EC , 2001 .

[6]  J. R Anchor.  Non-metallic mineral products , 2000 .

[7]  Francis X. Diebold,et al.  Unit-Root Tests Are Useful for Selecting Forecasting Models , 1999 .

[8]  J. Stock,et al.  A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series , 1998 .

[9]  Norman R. Swanson,et al.  A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks , 1997, Review of Economics and Statistics.

[10]  M. Delapierre.  The Case of France , 1997 .

[11]  Michael P. Clements,et al.  Multi-Step Estimation For Forecasting , 2009 .

[12]  Timo Teräsvirta,et al.  Modelling Economic Relationships with Smooth Transition Regressions , 1996 .

[13]  J. Stock,et al.  Evidence on Structural Instability in Macroeconomic Time Series Relations , 1994 .

[14]  C. Granger,et al.  Modelling Nonlinear Economic Relationships , 1995 .

[15]  G. C. Tiao,et al.  Robustness of maximum likelihood estimates for multi-step predictions: The exponential smoothing case , 1993 .

[16]  J. Stock,et al.  Efficient Tests for an Autoregressive Unit Root , 1992 .

[17]  D. Andrews,et al.  Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative , 1992 .

[18]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[19]  J. Nyblom.  Testing for the Constancy of Parameters over Time , 1989 .

[20]  John Geweke,et al.  A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series , 1984 .

[21]  Robert L. Winkler,et al.  The accuracy of extrapolation (time series) methods: Results of a forecasting competition , 1982 .

[22]  Stephen Taylor,et al.  Forecasting Economic Time Series , 1979 .

[23]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[24]  G. Gregory,et al.  A Critique of “Operational Research and Decision-Making” by Adelson and Norman , 1969 .