Unit root tests in the presence of an innovation variance break that has power against the mean break stationary alternative
We show that Perron's [Perron, P., 1990. Testing for a unit root in a time series with a changing mean. Journal of Business and Economic Statistics 8, 153-162] unit root test can be oversized when there is a break in the innovation variance. We propose a modified Perron test that maintains its size, and has power against the mean-break stationary alternative.
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Volume (Year): 79 (2009)
Issue (Month): 3 (February)
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- Perron, Pierre, 1997.
"Further evidence on breaking trend functions in macroeconomic variables,"
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"Testing for a Unit Root in a Time Series with a Changing Mean,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 8(2), pages 153-62, April.
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- Serena Ng & Pierre Perron, 1997.
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Boston College Working Papers in Economics
369, Boston College Department of Economics, revised 01 Sep 2000.
- Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
- Perron, Pierre & Vogelsang, Timothy J, 1992.
"Nonstationarity and Level Shifts with an Application to Purchasing Power Parity,"
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- Vogelsang, T.I. & Perron, P., 1991. "Nonstationary and Level Shifts With An Application To Purchasing Power Parity," Papers 359, Princeton, Department of Economics - Econometric Research Program.
- Kim, Tae-Hwan & Leybourne, Stephen & Newbold, Paul, 2002. "Unit root tests with a break in innovation variance," Journal of Econometrics, Elsevier, vol. 109(2), pages 365-387, August.
- Brooks, Chris & Rew, Alistair G, 2002. "Testing for a Unit Root in a Process Exhibiting a Structural Break in the Presence of GARCH Errors," Computational Economics, Society for Computational Economics, vol. 20(3), pages 157-76, December.
- Busetti, Fabio & Taylor, A M Robert, 2003. "Variance Shifts, Structural Breaks, and Stationarity Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 510-31, October.
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