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Estimation Under Inequality Constraints: Semiparametric Estimation of Conditional Duration Models

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  • Kulan Ranasinghe
  • Mervyn Silvapulle
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    Abstract

    This article proposes a semiparametric estimator of the parameter in a conditional duration model when there are inequality constraints on some parameters and the error distribution may be unknown. We propose to estimate the parameter by a constrained version of an unrestricted semiparametrically efficient estimator. The main requirement for applying this method is that the initial unrestricted estimator converges in distribution. Apart from this, additional regularity conditions on the data generating process or the likelihood function, are not required. Hence the method is applicable to a broad range of models where the parameter space is constrained by inequality constraints, such as the conditional duration models. In a simulation study involving conditional duration models, the overall performance of the constrained estimator was better than its competitors, in terms of mean squared error. A data example is used to illustrate the method.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/07474938.2011.553537
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

    Volume (Year): 30 (2011)
    Issue (Month): 4 ()
    Pages: 359-378

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    Handle: RePEc:taf:emetrv:v:30:y:2011:i:4:p:359-378

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    Related research

    Keywords: Approximating cone; Constrained inference; Convex Approximating cone; Convex parameter space; Order restricted inference; Projection estimator;

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