We propose a novel procedure, built within a Generalized Method of Moments framework, which exploits unpaired observations (singletons) to increase the efficiency of longitudinal fixed effect estimates. The approach allows increasing estimation efficiency, while properly tackling the bias due to unobserved time-invariant characteristics. We assess its properties by means of Monte Carlo simulations, and apply it to a traditional Total Factor Productivity regression, showing efficiency gains of approximately 8–9 percent.
Bruno, R. L., Magazzini, L., Stampini, M., Exploiting information from singletons in panel data analysis: A GMM approach, <<ECONOMICS LETTERS>>, 2020; 186 (N/A): 108519-108522. [doi:10.1016/j.econlet.2019.07.004] [https://hdl.handle.net/10807/226668]
Exploiting information from singletons in panel data analysis: A GMM approach
Bruno, Randolph LucaPrimo
Membro del Collaboration Group
;
2020
Abstract
We propose a novel procedure, built within a Generalized Method of Moments framework, which exploits unpaired observations (singletons) to increase the efficiency of longitudinal fixed effect estimates. The approach allows increasing estimation efficiency, while properly tackling the bias due to unobserved time-invariant characteristics. We assess its properties by means of Monte Carlo simulations, and apply it to a traditional Total Factor Productivity regression, showing efficiency gains of approximately 8–9 percent.
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