How successful are dynamic factor models at forecasting output and inflation? A meta‐analytic approach

Sandra Eickmeier · Christina Ziegler

137 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Statistical Methods and Applications

Abstract

Abstract This paper uses a meta‐analysis to survey existing factor forecast applications for output and inflation and assesses what causes large factor models to perform better or more poorly at forecasting than other models. Our results suggest that factor models tend to outperform small models, whereas factor forecasts are slightly worse than pooled forecasts. Factor models deliver better predictions for US variables than for UK variables, for US output than for euro‐area output and for euro‐area inflation than for US inflation. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre‐selecting the variables included in the dataset did not improve factor forecasts in the past. Finally, the factor estimation technique may matter as well. Copyright © 2008 John Wiley & Sons, Ltd.

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References

2000Practical Meta-Analysis 1994Comparing Predictive Accuracy 2002Determining the Number of Factors in Approximate Factor Models 2002Forecasting Using Principal Components From a Large Number of Predictors 2002Macroeconomic Forecasting Using Diffusion Indexes 2000The Generalized Dynamic-Factor Model: Identification and Estimation 2006Tests of Conditional Predictive Ability 1999Forecasting inflation

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