In two recent papers Enders and Lee (2008) and Becker et al. (2006) provide Lagrange multiplier and OLS de-trended unit root tests, and stationarity tests, respectively, which incorporate a Fourier approximation element in the deterministic component. Such an approach can prove useful in...

In this article we propose wild bootstrap implementations of the local generalized least squares (GLS) de-trended M and ADF unit root tests of Stock (1999), Ng and Perron (2001), and Elliott et al. (1996), respectively. The bootstrap statistics are shown to replicate the first-order asymptotic...

In a recent article, Xiao and Lima (2007) show numerically that the stationarity test of Kwiatkowski et al. (1992) has power close to size when the volatility of the innovation process follows a linear trend. In this article, highlighting published results in Cavaliere and Taylor (2005), we show...

In this paper we investigate the impact which starting values have upon the double differencing tests of Hasza and Fuller (1979, An. Stats.) and Sen and Dickey (1987, Jn.Bus.Ec.Stats.). We demonstrate that when bases on data which has been de-meaned, these tests are not exact similar to the...

We investigate the behaviour of rolling and recursive augmented Dickey-Fuller (ADF) tests against processes which display changes in persistence. We show that the power of the tests depend crucially on the window width and warm up parameter for the rolling and recursive procedures respectively,...

In this paper, we suggest a new set of regression-based statistics for testing the seasonal unit root null hypothesis. These tests are based on combining conventional Hylleberg et al. (1990) -type seasonal unit root test statistics calculated from both forward and reverse estimation of the...

Conventional unit root tests are known to be unreliable in the presence of permanent volatility shifts. In this paper, we propose a new approach to unit root testing which is valid in the presence of a quite general class of permanent variance changes which includes single and multiple (abrupt...