R-P. Berben
http://repub.eur.nl/ppl/9223/
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RePub, Erasmus University RepositoryUnit root tests and assymmetric adjustment
http://repub.eur.nl/pub/1558/
Mon, 29 Mar 1999 00:00:01 GMT<div>R-P. Berben</div><div>D.J.C. van Dijk</div>
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.Does the absence of cointegration explain the typical findings in long horizon regressions?
http://repub.eur.nl/pub/1555/
Thu, 31 Dec 1998 00:00:01 GMT<div>R-P. Berben</div><div>D.J.C. van Dijk</div>
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.