A nonparametric adjustment for tests of changing mean
When testing for a change in mean of a time series, the null hypothesis is no change in mean. However, a change in mean causes a bias in the estimation of serial correlation parameters. This bias can cause nonmonotonic power to the point that if the change is big enough, power can go to zero. In this paper, we show that a nonparametric correction can restore power. The procedure is illustrated with a small Monte Carlo experiment.
Volume (Year): 3 (2004)
Issue (Month): 34 ()
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- Perron, Pierre, 1989.
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Econometric Society, vol. 57(6), pages 1361-1401, November.
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Cowles Foundation Discussion Papers
1015, Cowles Foundation for Research in Economics, Yale University.
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- Vogelsang, Timothy J., 1997. "Wald-Type Tests for Detecting Breaks in the Trend Function of a Dynamic Time Series," Econometric Theory, Cambridge University Press, vol. 13(06), pages 818-848, December.
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