Forecasting by factors, by variables, by both or neither?
Jennifer Castle (),
Michael Clements and
David Hendry ()
Journal of Econometrics, 2013, vol. 177, issue 2, 305-319
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)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:177:y:2013:i:2:p:305-319
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