On the treatment of the weighted initial observation in the AR(1) regression model

Daniel Thornton

No 1984-003, Working Papers from Federal Reserve Bank of St. Louis

Abstract: This note shows that the ordinary least squares estimator of a first-order autoregressive model is always more efficient relative to the Cochrane-Orcutt estimator if the autocorrelation process has a finite past than if its past is infinite. This result cast doubt on the usual suggestion that it might be better to delete the initial observation rather than weight it if the autocorrelation process has a finite past.

Date: 1984
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