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Exploiting Information from Singletons in Panel Data Analysis: A GMM Approach

Randolph Bruno, Laura Magazzini and Marco Stampini

No 12465, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: unobserved heterogeneity; singletons; panel data; efficient estimation; GMM (search for similar items in EconPapers)
JEL-codes: C23 C33 C51 (search for similar items in EconPapers)
Pages: 12 pages
Date: 2019-07
New Economics Papers: this item is included in nep-ecm and nep-eff
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Published - published in: Economics Letters, 2020, 186, 108519

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Journal Article: Exploiting information from singletons in panel data analysis: A GMM approach (2020) Downloads
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