In this paper we consider the issue of testing a time series for a unit root in the possible presence of a break in a linear deterministic trend at some unknown point in the series. We propose aâ€¦ (More)

In this paper we propose a test of the null hypothesis of time series linearity against a nonlinear alternative, when uncertainty exists as to whether or not the series contains a unit root. Weâ€¦ (More)

Testing for the presence of a broken linear trend when the nature of the persistence in the data is unknown is not a trivial problem, since the test needs to be both asymptotically correctly sizedâ€¦ (More)

In this paper we focus on two major issues that surround testing for a unit root in practice, namely: (i) uncertainty as to whether or not a linear deterministic trend is present in the data, andâ€¦ (More)

In this paper we develop a simple test procedure for a linear trend which does not require knowledge of the form of serial correlation in the data, is robust to strong serial correlation, and has aâ€¦ (More)

In this paper we introduce a new test of the null hypothesis of no cointegration between a pair of time series. For a very simple generating model, our test compares favourably with theâ€¦ (More)

In this paper we analyse the impact of non-stationary volatility on the recently developed unit root tests which allow for a possible break in trend occurring at an unknown point in the sample,â€¦ (More)

Recent approaches to testing for a unit root when uncertainty exists over the presence and timing of a trend break employ break detection methods, so that a with-break unit root test is used only ifâ€¦ (More)

We consider testing for the presence of nonlinearities in the mean and/or trend of a time series, approximating the potential nonlinear behaviour using a Fourier function expansion. In contrast toâ€¦ (More)

We provide a joint treatment of two major problems that surround testing for a unit root in practice, namely uncertainty as to whether or not a linear deterministic trend is present in the data, andâ€¦ (More)