Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/230430 
Year of Publication: 
2018
Series/Report no.: 
Cardiff Economics Working Papers No. E2018/24
Publisher: 
Cardiff University, Cardiff Business School, Cardiff
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 of 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.
Subjects: 
Forecast Breaks
Statistical Decision Making
Central Banking
JEL: 
C53
E47
E58
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.