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A new approach for detecting shifts in forecast accuracy

Chiu, Ching-Wai (Jeremy), Hayes, Simon, Kapetanios, George and Theodoridis, Konstantinos 2019. A new approach for detecting shifts in forecast accuracy. International Journal of Forecasting 35 (4) , pp. 1596-1612. 10.1016/j.ijforecast.2019.01.008
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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. However, commonly-used statistical procedures implicitly place a lot of weight on type I errors (or false positives), which results in a relatively low power of the tests to identify forecast breakdowns in small samples. We develop a procedure which aims to capture 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, although 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 our choice of the critical values for each series not only to the in-sample properties of each series, but also to the way in which the series of forecast errors covary.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: Elsevier
ISSN: 0169-2070
Date of First Compliant Deposit: 30 January 2019
Date of Acceptance: 29 January 2019
Last Modified: 01 Nov 2019 16:25
URI: http://orca.cf.ac.uk/id/eprint/118946

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