Testing Stationarity in Small and Medium-Sized Samples when Disturbances are Serially Correlated

Kristian Jönsson

No 2006:20, Working Papers from Lund University, Department of Economics

Abstract: In this paper, we study the size distortions of the KPSS test for stationarity when serial correlation is present and samples are small and medium-sized. It is argued that two distinct sources of the size distortions can be identified. The first source is the finite-sample distribution of the long-run variance estimator used in the KPSS test, while the second source of the size distortions is the serial correlation not captured by the long-run variance estimator due to a too narrow choice of truncation lag parameter. When the relative importance of the two sources is studied, it is found that the size of the KPSS test can be reasonably well controlled if the finite-sample distribution of the KPSS test statistic, conditional on the time-series dimension and the truncation lag parameter, is used. Hence, finite-sample critical values, that can be applied in order to reduce the size distortions of the KPSS test, are supplied. When the power of the test is studied, it is found that the price paid for the increased size control is a lower raw power against a non-stationary alternative hypothesis.

Keywords: Stationarity Testing; Unit Root; Finite-Sample Inference; Long-Run Variance; Monte Carlo Simulation; Permanent Income Hypothesis; Private Consumption (search for similar items in EconPapers)
JEL-codes: C12 C14 C15 C22 E21 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2006-10-30, Revised 2009-11-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (1)

Published as Jönsson, Kristian, 'Testing Stationarity in Small and Medium-Sized Samples when Disturbances are Serially Correlated' in Oxford Bulletin of Economics and Statistics, 2011, pages 669-690.

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Journal Article: Testing Stationarity in Small‐ and Medium‐Sized Samples when Disturbances are Serially Correlated (2011)
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