Estimation and inference in the case of competing sets of estimating equations.

When there is uncertainty concerning the appropriate statistical model and corresponding estimators and inference methods, we use the Cressie-Read measure of divergence to define a semiparametric estimator, β[Graphic Character Omitted](α@), that combines plausible estimation problems. This estimatio...

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Publicado en:Journal of Econometrics Vol. 138; no. 2; pp. 513 - 532
Autores principales: Judge, George G., Mittelhammer, Ron C.
Formato: Artículo
Publicado: Elsevier Science June 2007
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        10.1016/j.jeconom.2006.05.007
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        atl: Estimation and inference in the case of competing sets of estimating equations.
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        au:
          Judge, George G.
          Mittelhammer, Ron C.
      su:
        Probability theory
        Information theory in economics
        Estimation theory
      sug:
        subj:
          Probability theory
          Information theory in economics
          Estimation theory
      ab: When there is uncertainty concerning the appropriate statistical model and corresponding estimators and inference methods, we use the Cressie-Read measure of divergence to define a semiparametric estimator, β[Graphic Character Omitted](α@), that combines plausible estimation problems. This estimation procedure identifies, conditional on the data, an optimal combination of competing estimators for the unknown parameters associated with the alternative plausible structural model specifications. The optimization is handled internally and avoids the tuning parameters usually necessary in problems of this type. To illustrate finite sample performance, an extensive sampling experiment is conducted to demonstrate the adaptive nature of the estimator for an array of data sampling specifications. Copyright (c) 2007 Elsevier B.V.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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