Is infrastructure capital really productive? Non-parametric modeling and data-driven model selection in a crosssectionally dependent panel framework

Antonio Musolesi, Giada Andrea Prete and Michel Simioni ()
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Antonio Musolesi: UniFE - Università degli Studi di Ferrara = University of Ferrara, UniFE - Università degli Studi di Ferrara = University of Ferrara
Giada Andrea Prete: UniFE - Università degli Studi di Ferrara = University of Ferrara
Michel Simioni: UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement, TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: This paper provides a broad replication of Calderón et al. (2015). We address some complex and relevant issues, namely functional form, non-stationary variables and cross-sectional dependence. In particular, by adopting the CCE framework, we consider both parametric-static and dynamic-and non-parametric specications, thus allowing for dierent degrees of exibility. Contrary to Calderón et al. (2015), we nd a lack of signicance of the infrastructure index, with an estimated elasticity very close to zero for all estimates. Moreover, by employing the data-driven model selection procedure proposed by Gioldasis et al. (2021), it is found that non-parametric specications provide the best predictive performance and that CCE models always overperform with respect to traditional panel data methods that employ cross-sectional demeaning to account for cross-sectional dependence.

Keywords: Cross-sectional dependence; Factor models; Moving block bootstrap; Non-parametric regression; Spline functions; Public capital hypothesis. (search for similar items in EconPapers)
Date: 2022-06-02
New Economics Papers: this item is included in nep-eff
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