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α: calibrated so average coauthorship-adjusted count equals average raw count
We study the finite sample properties of tests for structural changes in the trend function of a time series that do not require knowledge of the degree of persistence in the noise component. The tests of interest are the quasi-Feasible Generalized Least Squares (FGLS) procedure by Perron and Yabu (2009b) and the weighted average of the regression <italic>t</italic>-statistics by Harvey <italic>et al</italic>. (2009), both of which have the same limit distribution whether the noise component is stationary or has a unit-root. We analyse the finite sample size and power properties of these tests under a variety of Data-Generating Processes (DGPs). The results show that the Perron--Yabu test has greater power overall. With respect to the size, the Harvey--Leybourne--Taylor test exhibits larger size distortions unless a moving-average component is present. Using the Perron and Yabu procedure to test for structural changes in the trend function of long-run real exchange rates with respect to the US dollar indicates that for 17 out of 19 countries, the series have experienced a shift in trend since the late nineteenth century.