Robust approaches to forecasting
Jennifer Castle (),
Michael Clements and
David Hendry ()
International Journal of Forecasting, 2015, vol. 31, issue 1, 99-112
We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium-correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, impulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods are likely to perform well. The robust methods are applied to forecasting US GDP using autoregressive models, and also to autoregressive models with factors extracted from a large dataset of macroeconomic variables. We consider forecasting performance over the Great Recession, and over an earlier more quiescent period.
Keywords: Forecast biases; Smoothed forecasting devices; Factor models; GDP forecasts; Location shifts (search for similar items in EconPapers)
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Working Paper: Robust Approaches to Forecasting (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:31:y:2015:i:1:p:99-112
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