Instrument Selection in Panel Data Models with Endogeneity: A Bayesian Approach

Álvaro Herce () and Manuel Salvador
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Álvaro Herce: Department of Applied Economy, Faculty of Business and Economics, University of Zaragoza, Gran Vía 2, 50005 Zaragoza, Spain
Manuel Salvador: Department of Applied Economy, Faculty of Business and Economics, University of Zaragoza, Gran Vía 2, 50005 Zaragoza, Spain

Econometrics, 2024, vol. 12, issue 4, 1-35

Abstract: This paper proposes the use of Bayesian inference techniques to search for and obtain valid instruments in dynamic panel data models where endogenous variables may exist. The use of Principal Component Analysis (PCA) allows for obtaining a reduced number of instruments in comparison to the high number of instruments commonly used in the literature, and Monte Carlo Markov Chain (MCMC) methods enable efficient exploration of the instrument space, deriving accurate point estimates of the elements of interest. The proposed methodology is illustrated in a simulated case and in an empirical application, where the partial effect of a series of determinants on the attraction of international bank flows is quantified. The results highlight the importance of promoting and developing the private sector in these economies, as well as the importance of maintaining good levels of creditworthiness.

Keywords: endogeneity; Bayesian methods; MCMC; instrumental variables selection; international bank flows (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2024
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