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Robust Forecast Methods and Monitoring during Structural Change
Author(s) -
Eklund Jana,
Kapetanios George,
Price Simon
Publication year - 2013
Publication title -
the manchester school
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.361
H-Index - 42
eISSN - 1467-9957
pISSN - 1463-6786
DOI - 10.1111/manc.12011
Subject(s) - econometrics , series (stratigraphy) , sample (material) , economics , structural break , consensus forecast , computer science , geology , paleontology , chemistry , chromatography
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 M onte C arlo 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.