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Forecasting by factors, by variables, or both?

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  • Jennifer Castle
  • David Hendry

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

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 600.

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Date of creation: 01 Apr 2012
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Handle: RePEc:oxf:wpaper:600

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

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References

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Cited by:
  1. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.
  2. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper Series 53_12, The Rimini Centre for Economic Analysis.

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