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Uniformly adaptive estimation for models with arma errors

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  • Dougas Steigerwald

Abstract

A semiparametric estimator based on an unknown density isuniformly adaptive if the expected loss of the estimator converges to the asymptotic expected loss of the maximum liklihood estimator based on teh true density (MLE), and if convergence does not depend on either the parameter values or the form of the unknown density. Without uniform adaptivity, the asymptotic expected loss of the MLE need not approximate the expected loss of a semiparametric estimator for any finite sample I show that a two step semiparametric estimator is uniformly adaptive for the parameters of nonlinear regression models with autoregressive moving average errors.

Suggested Citation

  • Dougas Steigerwald, 1997. "Uniformly adaptive estimation for models with arma errors," Econometric Reviews, Taylor & Francis Journals, vol. 16(4), pages 393-409.
    • Handle: RePEc:taf:emetrv:v:16:y:1997:i:4:p:393-409
    DOI: 10.1080/07474939708800395
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    References listed on IDEAS

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    1. Steigerwald, Douglas G., 1992. "Adaptive estimation in time series regression models," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 251-275.
    2. Bruce Lind & George Roussas, 1977. "Cramér-type conditions and quadratic mean differentiability," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 29(1), pages 189-201, December.
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