Musolesi, AntonioIdRef, Prete, Giada Andrea and Simioni, MichelIdRefORCIDORCID: https://orcid.org/0000-0002-4516-8750 (2025) Is infrastructure capital really productive? Nonparametric modeling and data-driven model selection in a cross-sectionally dependent panel framework. Journal of Productivity Analysis.

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Identification Number : 10.1007/s11123-025-00779-x

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.

Item Type: Article
Language: English
Date: September 2025
Refereed: Yes
Uncontrolled Keywords: Cross-sectional dependence, factor models, moving block bootstrap, nonparametric regression, spline functions, public capital hypothesis
JEL Classification: C23 - Models with Panel Data
C5 - Econometric Modeling
O4 - Economic Growth and Aggregate Productivity
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 07 Oct 2025 07:24
Last Modified: 07 Oct 2025 07:24
OAI Identifier: oai:tse-fr.eu:130973
URI: https://publications.ut-capitole.fr/id/eprint/51245

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