Is infrastructure capital really productive? Nonparametric modeling and data-driven model selection in a cross-sectionally dependent panel framework

Antonio Musolesi (), Giada Andrea Prete and Michel Simioni
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Antonio Musolesi: DEM and SEEDS
Giada Andrea Prete: Statistics and Research
Michel Simioni: University of Montpellier

Journal of Productivity Analysis, 2025, vol. 64, issue 3, No 13, 439-455

Abstract: Abstract This paper examines the contribution of infrastructure to aggregate productivity. We address some complex and relevant issues, namely functional form, nonstationary variables and cross-sectional dependence. We adopt the CCE framework and consider both parametric and nonparametric specifications, thus allowing for different degrees of flexibility. We also employ a data-driven model selection procedure based on moving block bootstrap to choose among alternative specifications. It is found that nonparametric specifications provide the best predictive performance and that CCE models always overperform with respect to traditional panel data methods. Furthermore, we find a lack of significance of the infrastructure index, with an estimated elasticity very close to zero for all estimates.

Keywords: Cross-sectional dependence; factor models; moving block bootstrap; nonparametric regression; spline functions; public capital hypothesis (search for similar items in EconPapers)
JEL-codes: C23 C5 O4 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11123-025-00779-x

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