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Godambe estimating functions and asymptotic optimal inference

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  • Hwang, S.Y.
  • Basawa, I.V.

Abstract

Godambe (1985) introduced a class of optimum estimating functions which can be regarded as a generalization of quasilikelihood score functions. The "optimality" established by Godambe (1985) within a certain class is for estimating functions and it is based on finite samples. The question that arises naturally is what (if any) asymptotic optimality properties do the estimators and tests based on optimum estimating functions possess. In this paper, we establish, via presenting a convolution theorem, asymptotic optimality of estimators and tests obtained from Godambe optimum estimating functions. It is noted that we do not require the knowledge of the likelihood function.

Suggested Citation

  • Hwang, S.Y. & Basawa, I.V., 2011. "Godambe estimating functions and asymptotic optimal inference," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1121-1127, August.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:8:p:1121-1127
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    References listed on IDEAS

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    1. Young Hwang, Sun & Basawa, I. V., 1993. "Asymptotic optimal inference for a class of nonlinear time series models," Stochastic Processes and their Applications, Elsevier, vol. 46(1), pages 91-113, May.
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