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Displaying results 1 to 3 of 3.

  1. Return to experience and initial wage level
    do low wage workers catch up?
    Author: Sørensen, Kenneth L.; Vejlin, Rune
    Published: 2012
    Publisher:  Univ. of Aarhus, Dep. of Economics, Aarhus

    Location:
    Niedersächsische Staats- und Universitätsbibliothek Göttingen
    Inter-library loan:
    No inter-library loan

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    Keine Speicherung
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    Content information
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Economics working paper ; 2012,02
    Subjects: Wage growth; initial wage; return to experience; nonparametric estimation
    Scope: Online-Ressource (33 S.)
  2. Opportunity cost estimation of ecosystem services
    Published: 2012
    Publisher:  School of Social Sciences, University of Manchester, Manchester

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Economics discussion paper series : EDP ; 1222
    Subjects: opportunity costs; ecosystem services; biodiversity; non-separability; nonparametric estimation; trade-offs; comparative advantage
    Scope: Online-Ressource (PDF-Datei: 38 S.), graph. Darst.
  3. Adaptive nonparametric instrumental variables estimation
    empirical choice of the regularisation parameter
    Author: Horowitz, Joel
    Published: 2012
    Publisher:  Centre for Microdata Methods and Practice, London

    In nonparametric instrumental variables estimation, the mapping that identifies the function of interest, g say, is discontinuous and must be regularised (that is, modified) to make consistent estimation possible. The amount of modification is... more

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 243 (2013,30)
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    In nonparametric instrumental variables estimation, the mapping that identifies the function of interest, g say, is discontinuous and must be regularised (that is, modified) to make consistent estimation possible. The amount of modification is contolled by a regularisation parameter. The optimal value of this parameter depends on unknown population characteristics and cannot be calculated in applications. Theoretically justified methods for choosing the regularisatoin parameter empirically in applications are not yet available. This paper presents such a method for use in series estimation, where the regularisation parameter is the number of terms in a series approximation to g. The method does not required knowledge of the smoothness of g or of other unknown functions. It adapts to their unknown smoothness. The estimator of g based on the empirically selected regularisation parameter converges in probabillity at a rate that is at least as fast as the asymptotically optimal rate multiplied by (logn)1/2, where n is the sample size. The asymptotic integrated mean-square error (AIMSE) of the estimator is within a specified factor of the optimal AIMSE.

     
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    Content information
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/79530
    Series: Cemmap working paper / Centre for Microdata Methods and Practice ; 30/13
    Subjects: Ill-posed inverse problem; regularization; sieve estimation; series estimation; nonparametric estimation
    Scope: Online-Ressource (45 S.), graph. Darst.