Testing for a Forecast Accuracy Breakdown under Long Memory

TL;DR

This paper introduces a new statistical test to detect forecast accuracy breakdowns in long memory time series, with applications to energy prices, supported by theoretical, simulation, and empirical evidence.

Contribution

It develops a robust double sup-Wald test and a long memory robust CUSUM test for detecting structural breaks in forecast accuracy in long memory series.

Findings

Detected forecast break in European electricity prices during the 2021 energy crisis.

The test shows good size and power properties in simulations.

Mixed results for forecast breaks in U.S. energy prices.

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.