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This paper develops an asymptotic theory of estimation and inference in 'cointegrated' regression models with errors displaying nonstationary variances. Least squares estimates are shown to be consistent at a T1/2 Tate. Hypothesis testing requires the use of a robust covariance matrix estimate, in contrast to earlier work on cointegrated regressions. The inference theory is not nuisance-free, but preliminary investigations indicate that approximation by the normal distribution may be adequate in practice.
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DEPENDENT HETEROGENEOUS PROCESSES
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