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- W2974321723 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." @default.
- W2974321723 created "2019-09-26" @default.
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- W2974321723 date "2018-01-01" @default.
- W2974321723 modified "2023-09-23" @default.
- W2974321723 title "The Joint Estimate of Singleton and Longitudinal Observations: a GMM Approach for Improved Efficiency" @default.
- W2974321723 hasPublicationYear "2018" @default.
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