Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/259936 
Year of Publication: 
2006
Series/Report no.: 
Working Paper No. 2006:23
Publisher: 
Lund University, School of Economics and Management, Department of Economics, Lund
Abstract: 
In the current paper, the finite-sample stability of various implementations of the KPSS test is studied. The implementations considered differ in how the so-called long-run variance is estimated under the null hypothesis. More specifically, the effects that the choice of kernel, the value of the bandwidth parameter and the application of a prewhitening filter have on the KPSS test are investigated. It is found that the finite-sample distribution KPSS test statistic can be very unstable when the Quadratic Spectral kernel is used and/or a prewhitening filter is applied. The instability manifests itself through making the small-sample distribution of the test statistic sensitive to the specific process that generates the data under the null hypothesis. This in turn implies that the size of the test can be hard to control. For the cases investigated in the current paper, it turns out that using the Bartlett kernel in the long-run variance estimation renders the most stable test. By supplying an empirical application, we illustrate the adverse effects that can occur when care is not taken in choosing what test implementation to employ when testing for stationarity in small-sample situations.
Subjects: 
Stationarity
Unit root
KPSS test
Size distortion
Long-run variance
Monte Carlo simulation
Private consumption
Permanent Income Hypothesis
JEL: 
C12
C13
C14
C15
C22
E21
Document Type: 
Working Paper

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