A new approach for detecting shifts in forecast accuracy

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Cardiff Economics Working Papers ; No. E2018/24

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Money and Interest Rates: Forecasting and Simulation: Models and Applications ; Central Banks and Their Policies
• Subject: Forecast Breaks ; Statistical Decision Making ; Central Banking

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Chiu,Ching-Wai ; Hayes, Simon ; Kapetanios, George ; Theodoridis, Konstantinos
• Event: Veröffentlichung
• (who): Cardiff University, Cardiff Business School
• (where): Cardiff
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/230430
• Last update: 10.03.2025, 11:44 AM CET