Testing for a Forecast Accuracy Breakdown under Long Memory

Jannik Kreye and Philipp Sibbertsen

Papers from arXiv.org

Abstract: We propose a test to detect a forecast accuracy breakdown in a long memory time series and provide theoretical and simulation evidence on the memory transfer from the time series to the forecast residuals. The proposed method uses a double sup-Wald test against the alternative of a structural break in the mean of an out-of-sample loss series. To address the problem of estimating the long-run variance under long memory, a robust estimator is applied. The corresponding breakpoint results from a long memory robust CUSUM test. The finite sample size and power properties of the test are derived in a Monte Carlo simulation. A monotonic power function is obtained for the fixed forecasting scheme. In our practical application, we find that the global energy crisis that began in 2021 led to a forecast break in European electricity prices, while the results for the U.S. are mixed.

Date: 2024-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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Working Paper: Testing for a Forecast Accuracy Breakdown under Long Memory (2024)
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