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Asymptotic Covariance Matrix of Procedures for Linear Regression in the Presence of First-Order Autoregressive Disturbances
J. Phillip Cooper
Abstract
The methods of Cochrane and Orcutt orr Hildreth and Lu to correct linear regressions for first-order autoregression in the disturbances, as usually implemented, underestimate the standard errors of the regression coefficients whenever a lagged dependent variable is included. A convenient transformation is derived from the information matrix to remove this bias. The asymptotic standard error of the estimated serial coefficient is a useful coproduct of the analysis.
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