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An Adjusted Maximum Likelihood Estimator of Autocorrelation in Disturbances

In: Econometrics and Economic Theory

Author

Listed:
  • Clifford Hildreth

    (The University of Minnesota
    The University of Iowa)

  • Warren T. Dent

    (The University of Minnesota
    The University of Iowa)

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.

Suggested Citation

  • Clifford Hildreth & Warren T. Dent, 1974. "An Adjusted Maximum Likelihood Estimator of Autocorrelation in Disturbances," Palgrave Macmillan Books, in: Willy Sellekaerts (ed.), Econometrics and Economic Theory, chapter 1, pages 3-25, Palgrave Macmillan.
    • Handle: RePEc:pal:palchp:978-1-349-01936-6_1
    DOI: 10.1007/978-1-349-01936-6_1
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