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The Joint Estimate of Singleton and Longitudinal Observations: a GMM Approach for Improved Efficiency

Author

Listed:
  • Randolph Luca Bruno

    (School of Slavonic and East European Studies, University College London)

  • Laura Magazzini

    (Department of Economics (University of Verona))

  • Marco Stampini

    (Social Protection and Health Division, Inter-American Development Bank)

Abstract

We devise an innovative methodology that allows exploiting information from singleton and longitudinal observations for the estimation of fixed effects panel data models. The approach can be applied to join cross-sectional data and longitudinal data, in order to increase estimation efficiency, while properly tackling the potential bias due to unobserved individual characteristics. Estimation is framed within the GMM context and we assess its properties by means of Monte Carlo simulations. The method is applied to an unbalanced panel of firm data to estimate a Total Factor Productivity regression based on the renown Business Environment and Enterprise Performance Survey (BEEPs) database. Under the assumption that the relationship between observed and unobserved characteristics is homogeneous across singleton and longitudinal observations (or across different samples), information from longitudinal data is used to "clean" the bias in the unpaired sample of singletons. This reduces the standard errors of the estimation (in our application, by approximately 8-9 percent) and has the potential to increase the significance of the coefficients.

Suggested Citation

  • Randolph Luca Bruno & Laura Magazzini & Marco Stampini, 2018. "The Joint Estimate of Singleton and Longitudinal Observations: a GMM Approach for Improved Efficiency," Working Papers 04/2018, University of Verona, Department of Economics.
    • Handle: RePEc:ver:wpaper:04/2018
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Panel Data; Efficient Estimation; Unobserved Heterogeneity; GMM;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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