An Adjusted Maximum Likelihood Estimator of Autocorrelation in Disturbances

Clifford Hildreth and Warren T. Dent
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Clifford Hildreth: The University of Minnesota
Warren T. Dent: The University of Minnesota

Chapter 1 in Econometrics and Economic Theory, 1974, pp 3-25 from Palgrave Macmillan

Abstract: Abstract Although it has been shown [9] that the maximum likelihood (M.L.) estimator of the autocorrelation coefficient in linear models with autoregressive disturbances is asymptotically unbiased, several Monte Carlo studies [8], [6], [15] suggest that finite sample bias is usually large enough to be of some concern. In the next section an approximation to the bias is developed and used to obtain an adjusted estimator with substantial smaller bias. Section 3 presents the results of applying the adjusted M.L. estimator, the unadjusted M.L., and two other estimators to Monte Carlo data. Some interpretations and conjectures comprise Section 4 and computing procedures are discussed in Section 5. The remainder of this section contains a brief sketch of maximum likelihood estimation.

Keywords: Maximum Likelihood Estimator; Monte Carlo Study; Autocorrelation Coefficient; Extreme Difference; Monte Carlo Data (search for similar items in EconPapers)
Date: 1974
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Persistent link: https://EconPapers.repec.org/RePEc:pal:palchp:978-1-349-01936-6_1

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DOI: 10.1007/978-1-349-01936-6_1

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