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On estimator efficiency in stochastic processes

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  • Sweeting, Trevor

Abstract

It is shown, under mild regularity conditions on the random information matrix, that the maximum likelihood estimator is efficient in the sense of having asymptotically maximum probability of concentration about the true parameter value. In the case of a single parameter, the conditions are improvements of those used by Heyde (1978). The proof is based on the idea of maximum probability estimators introduced by Weiss and Wolfowitz (1967).

Suggested Citation

  • Sweeting, Trevor, 1983. "On estimator efficiency in stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 15(1), pages 93-98, June.
    • Handle: RePEc:eee:spapps:v:15:y:1983:i:1:p:93-98
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    Cited by:

    1. Phillips, P C B, 1991. "Optimal Inference in Cointegrated Systems," Econometrica, Econometric Society, vol. 59(2), pages 283-306, March.

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