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

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  • Daniel L. Thornton

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.

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File URL: http://research.stlouisfed.org/wp/1984/1984-003.pdf
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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 1984-003.

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Date of creation: 1984
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Handle: RePEc:fip:fedlwp:1984-003

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