http://repub.eur.nl/res/aut/9223/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/1558/ 1999-03-29T00:00:00Z Standard unit root tests are misspecified in case the variable of interest is stationary but displays asymmetric adjustment towards its long-run equilibrium and, consequently, may suffer from a lack of power against such alternatives. This observation recently has aroused interest in developing test statistics which can be used to test the null hypothesis of a unit root against the alternative of stationarity with asymmetric adjustment. In this paper we reconsider the test statistics put forward by Enders and Granger (1998). We point out an important deficiency of their tests and develop an alternative one which is based on more solid statistical grounds. Monte Carlo experiments demonstrate that our new test outperforms standard unit roots and the tests of Enders and Granger (1998) in terms of power against the alternative of interest. An empirical illustration involving the forward premium is provided to demonstrate the practical usefulness of our test statistic. http://repub.eur.nl/res/pub/1555/ 1998-12-31T00:00:00Z One of the stylized facts in financial and international economics is that of increasing predictability of variables such as exchange rates and stock returns at longer horizons. This fact is based upon applications of long horizon regressions, from which the typical findings are that the point estimates of the regression parameter, the associated t-statistic, and the regression R^2 all tend to increase as the horizon increases. Such long horizon regression analyses implicitly assume the existence of cointegration between the variables involved. In this paper, we investigate the consequences of dropping this assumption. In particular, we look upon the long horizon regression as a conditional error-correction model and interpret the test for long horizon predictability as a single equation test for cointegration. We derive the asymptotic distributions of the estimator of the regression parameter and its t-statistic for arbitrary horizons, under the null hypothesis of no cointegration. It is shown that these distributions provide an alternative explanation for at least part of the typical findings. Furthermore, the distributions are used to derive a Phillips-Perron type correction to the ordinary least-squares t-statistic in order to endow it with a stable size for given, arbitrary, horizon. A local asymptotic power analysis reveals that the power of long horizon regression tests does not increase with the horizon. Exchange rate data are used to demonstrate the empirical relevance of our theoretical results.