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Further results on bias in dynamic unbalanced panel data models with an application to firm R&D investment

Author

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  • Lokshin, Boris

    (UNU-MERIT, and Maastricht University)

Abstract

This paper extends the LSDV bias-corrected estimator in [Bun, M., Carree, M.A. 2005. Bias-corrected estimation in dynamic panel data models, Journal of Business and Economic Statistics, 23(2): 200-10] to unbalanced panels and discusses the analytic method of obtaining the solution. Using a Monte Carlo approach the paper compares the performance of this estimator with three other available techniques for dynamic panel data models. Simulation reveals that LSDV-bc estimator is a good choice except for samples with small T, where it may be unpractical. The methodology is applied to examine the impact of internal and external R&D on labor productivity in an unbalanced panel of innovating firms.

Suggested Citation

  • Lokshin, Boris, 2008. "Further results on bias in dynamic unbalanced panel data models with an application to firm R&D investment," MERIT Working Papers 2008-039, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    • Handle: RePEc:unm:unumer:2008039
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    File URL: https://unu-merit.nl/publications/wppdf/2008/wp2008-039.pdf
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    Cited by:

    1. Fabrice Murtin & Romain Wacziarg, 2014. "The democratic transition," Journal of Economic Growth, Springer, vol. 19(2), pages 141-181, June.
    2. Leandro D�Aurizio & Stefano Iezzi, 2011. "Investment forecasting with business survey data," Temi di discussione (Economic working papers) 832, Bank of Italy, Economic Research and International Relations Area.
    3. repec:spo:wpmain:info:hdl:2441/5m0od0o9jn9pqbdmos7fpt28hg is not listed on IDEAS
    4. repec:hal:spmain:info:hdl:2441/5m0od0o9jn9pqbdmos7fpt28hg is not listed on IDEAS

    More about this item

    Keywords

    Bias Correction; Unbalanced Panel Data; GMM; Dynamic Models;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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