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

Randolph Bruno (), Laura Magazzini and Marco Stampini

No 04/2018, Working Papers from University of Verona, Department of Economics

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.

Keywords: Panel Data; Efficient Estimation; Unobserved Heterogeneity; GMM (search for similar items in EconPapers)
JEL-codes: C23 C33 C51 (search for similar items in EconPapers)
Date: 2018-05
New Economics Papers: this item is included in nep-ecm and nep-eff
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