Publication: Fractional cointegrating regressions in the presence of linear time trends
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1998-01
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Abstract
We consider regressions of nonstationary fractionally integrated variables dominated by linear time trends. The regression errors are short memory, long memory or even nonstationary, and hence allow for a very flexible cointegration model. In case of simple regressions, least squares estimation gives rise to limiting normal distribucions independently of the order of integration of the regressor, whereas the customary t-statistics diverge. We also investigate the possibility of testing for mean reverting equilibrium deviations by means of a residual-based log-periodogram regression. Asymptotic results become more complicated in the multivariate case.
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Nonstationary regressors, Stationary or nonstationary errors, Limiting normality, Residual-based cointegration testing