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A new approach for detecting shifts in forecast accuracy

机译：检测预测准确性变化的新方法

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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 I 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. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：预测在以通胀为目标的中央银行（例如，英格兰银行）中发挥着关键作用。模型的预测性能中断可能会导致重要的政策成本。但是，常用的统计程序隐式地对I型错误（或误报）施加了很大的压力，这导致测试用于识别小样本中的预测细分的测试功能相对较低。我们制定了一个程序，旨在捕获错过休息的政策成本。我们使用基于数据的规则来确定测试规模，以最佳地权衡与误报相关的成本！那些由于中断而导致的检测时间过长。通过这样做，我们还可以将预测误差明确地研究为多变量系统。尽管在预测文献中经常忽略不同序列的预测误差之间的协方差，但它不仅使我们能够考虑在多变量环境中进行测试，而且还提高了测试能力。结果，我们不仅可以针对每个系列的样本内属性，还可以针对系列预测误差的变化方式，来选择每个系列的临界值。 （C）2019国际预报员学会。由Elsevier B.V.发布。保留所有权利。

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