Many unit root and cointegration tests require an estimate of the spectral density function at frequency zero at some process. Kernel estimators based on weighted sums of autocovariances constructed using estimated residuals from an AR(1) regression are commonly used. However, it is known that with substantially correlated errors, the OLS estimate of the AR(1) parameter is severely biased. In this paper, we first show that this least squares bias induces a significant increase in the bias and mean-squared error of kernel-based estimators.
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Paper provided by Centre interuniversitaire de recherche en économie quantitative, CIREQ in its series Cahiers de recherche with number
9611.
Find related papers by JEL classification: C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Other
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