A new approach for detecting shifts in forecast accuracy

Ching-Wai (Jeremy) Chiu, Simon Hayes (), George Kapetanios and Konstantinos Theodoridis
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Simon Hayes: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH

No 721, Bank of England working papers from Bank of England

Abstract: Forecasts play a critical role at inflation-targeting central banks, such as the Bank of England. Breaks in the forecast performance of a model can potentially incur important policy costs. Commonly used statistical procedures, however, implicitly put a lot of weight on type I errors (or false positives), which result in a relatively low power of tests to identify forecast breakdowns in small samples. We develop a procedure which aims at capturing the policy cost of missing a break. We use data-based rules to find the test size that optimally trades off the costs associated with false positives with those that can result from a break going undetected for too long. In so doing, we also explicitly study forecast errors as a multivariate system. The covariance between forecast errors for different series, though often overlooked in the forecasting literature, not only enables us to consider testing in a multivariate setting but also increases the test power. As a result, we can tailor the choice of the critical values for each series not only to the in-sample properties of each series but also to how the series for forecast errors covary.

Keywords: Forecast breaks; statistical decision making; central banking (search for similar items in EconPapers)
JEL-codes: C53 E47 E58 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2018-04-13
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-for and nep-mac
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Related works:
Journal Article: A new approach for detecting shifts in forecast accuracy (2019)
Working Paper: A New Approach for Detecting Shifts in Forecast Accuracy (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0721

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