Some comparisons of the relative power of simple tests for structural change in regression models

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Abstract
The power of Chow, linear, predictive failure and cusum of squares tests to detect structural change is compared in a two‐variable random walk model and a once‐for‐all parameter shift model. In each case the linear test has greatest power, followed by the Chow test. It is suggested that the linear test be used as the basic general test for structural change in time series data, and tests of forecasting performance be confined to the last few observations. Analysis of recursive residuals and recursive parameter estimates should be regarded as forms of exploratory data analysis and tools for understanding discrepancies with previous results rather than a basis for formal tests of structural change.