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Robust Forecast Methods and Monitoring during Structural Change
Article first published online: 1 AUG 2013
© 2013 The University of Manchester and John Wiley & Sons Ltd
The Manchester School
Special Issue: Structural Breaks and Monetary Policy
Volume 81, Issue Supplement S3, pages 3–27, October 2013
How to Cite
Eklund, J., Kapetanios, G. and Price, S. (2013), Robust Forecast Methods and Monitoring during Structural Change. The Manchester School, 81: 3–27. doi: 10.1111/manc.12011
- Issue published online: 30 OCT 2013
- Article first published online: 1 AUG 2013
- Manuscript Revised: 31 JAN 2013
- Manuscript Received: 11 NOV 2011
We examine how to forecast after a recent break, introducing a new approach, monitoring for change and then combining forecasts from a model using the full sample and another using post-break data. We compare this to some robust techniques: rolling regressions, forecast averaging over all possible windows and exponentially weighted forecasts. We examine relative efficacy with Monte Carlo experiments given single deterministic or multiple stochastic location shifts, and for many UK and US macroeconomic series. No single method is uniformly superior. Monitoring brings only small improvements, so robust methods are preferred.