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The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models

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  • Mohnen, Pierre

    ()
    (UNU-MERIT and University of Maastricht)

  • Raymond, Wladimir

    ()
    (University of Maastricht)

  • Palm, Franz

    ()
    (University of Maastricht)

  • Schim van der Loeff, Sybrand

    ()
    (University of Maastricht)

Abstract

This paper proposes a method to implement maximum likelihood estimation of the dynamic panel data type 2 and 3 tobit models. The likelihood function involves a two-dimensional indefinite integral evaluated using "two-step" Gauss-Hermite quadrature. A Monte Carlo study shows that the quadrature works well in finite sample for a number of evaluation points as small as two. Incorrectly ignoring the individual effects, or the dependence between the initial conditions and the individual effects results in an overestimation of the coefficients of the lagged dependent variables. An application to incremental and radical product innovations by Dutch business firms illustrates the method.

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Bibliographic Info

Paper provided by United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT) in its series MERIT Working Papers with number 007.

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Date of creation: 2007
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Handle: RePEc:unm:unumer:2007007

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Keywords: panel data; maximum likelihood estimator; dynamic models; sample selection;

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References

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Cited by:
  1. Peter Egger & Michael Pfaffermayr, 2011. "Structural Estimation of Gravity Models with Path-Dependent Market Entry," FIW Research Reports series, FIW III-007, FIW.
  2. Giannetti, Caterina, 2012. "Relationship lending and firm innovativeness," Journal of Empirical Finance, Elsevier, Elsevier, vol. 19(5), pages 762-781.

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