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

Author

Listed:
  • Wladimir Raymond
  • Pierre Mohnen
  • Franz Palm
  • Sybrand Schim van der Loeff
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.

Suggested Citation

  • Wladimir Raymond & Pierre Mohnen & Franz Palm & Sybrand Schim van der Loeff, 2007. "The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models," CESifo Working Paper Series 1992, CESifo.
    • Handle: RePEc:ces:ceswps:_1992
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Sergi Jiménez-Martín & José M. Labeaga & Majid al Sadoon, 2020. "Consistent estimation of panel data sample selection models," Working Papers 2020-06, FEDEA.
    2. Costa-Campi, M.T. & Duch-Brown, N. & García-Quevedo, J., 2014. "R&D drivers and obstacles to innovation in the energy industry," Energy Economics, Elsevier, vol. 46(C), pages 20-30.
    3. Wladimir Raymond & Pierre Mohnen & Franz Palm & Sybrand Schim van der Loeff, 2007. "The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models," CESifo Working Paper Series 1992, CESifo.
    4. Amegashie, J. Atsu & Ouattara, Bazoumanna & Strobl, Eric, 2007. "Moral Hazard and the Composition of Transfers: Theory with an Application to Foreign Aid," MPRA Paper 3158, University Library of Munich, Germany, revised 06 May 2007.
    5. Costa-Campi, M.T. & Duch-Brown, N. & García-Quevedo, J., 2014. "R&D drivers and obstacles to innovation in the energy industry," Energy Economics, Elsevier, vol. 46(C), pages 20-30.
    6. Pfaffermayr, Michael & Egger, Peter, 2011. "Structural Estimation of Gravity Models with Path-dependent Market Entry," CEPR Discussion Papers 8458, C.E.P.R. Discussion Papers.
    7. Giannetti, Caterina, 2012. "Relationship lending and firm innovativeness," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 762-781.
    8. repec:wsr:ecbook:2011:i:iii-007 is not listed on IDEAS
    9. Nestor Duch-Brown & Andrea de Panizza & Ibrahim Kholilul Rohman, 2016. "Innovation and productivity in a S&T intensive sector: the case of Information industries in Spain," JRC Research Reports JRC101847, Joint Research Centre.
    10. Diana Suárez, 2015. "This paper analyzes changes in the firm’s innovative strategy and how this impacts firm’s performance. The methodology is based on a cluster analysis over 800 Argentinean manufacturing firms with info," Globelics Working Paper Series 2015-04, Globelics - Global Network for Economics of Learning, Innovation, and Competence Building Systems, Aalborg University, Department of Business and Management.

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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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