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Forecasting by factors, by variables, by both or neither?

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  • Castle, Jennifer L.
  • Clements, Michael P.
  • Hendry, David F.

Abstract

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation. A forecast-error taxonomy for factor models highlights the impacts of location shifts on forecast-error biases. Forecasting US GDP over 1-, 4- and 8-step horizons using the dataset from Stock and Watson (2009) updated to 2011:2 shows factor models are more useful for nowcasting or short-term forecasting, but their relative performance declines as the forecast horizon increases. Forecasts for GDP levels highlight the need for robust strategies, such as intercept corrections or differencing, when location shifts occur as in the recent financial crisis.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 177 (2013)
Issue (Month): 2 ()
Pages: 305-319

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Handle: RePEc:eee:econom:v:177:y:2013:i:2:p:305-319

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Model selection; Factor models; Forecasting; Impulse-indicator saturation; Autometrics;

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References

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Cited by:
  1. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.

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