Forecasting by factors, by variables, or both?
Jennifer Castle () and
David Hendry ()
Authors registered in the RePEc Author Service: Michael Peter Clements
No 600, Economics Series Working Papers from University of Oxford, Department of Economics
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.
Keywords: Model selection; Factor models; Forecasting; Impulse-indicator saturation; Autometrics (search for similar items in EconPapers)
JEL-codes: C51 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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