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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Antonio Musolesi & Giada Andrea Prete & Michel Simioni, 2022. "Is infrastructure capital really productive? Non-parametric modeling and data-driven model selection in a crosssectionally dependent panel framework," Working Papers hal-03685558, HAL.
    • Handle: RePEc:hal:wpaper:hal-03685558
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-03685558
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    Keywords

    Cross-sectional dependence; Factor models; Moving block bootstrap; Non-parametric regression; Spline functions; Public capital hypothesis.;
    All these keywords.

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