Musolesi, Antonio, Prete, Giada Andrea and Simioni, Michel
ORCID: 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.
This is the latest version of this item.
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 |
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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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Is infrastructure capital really productive? Non-parametric modeling and data-driven model selection in a cross-sectionally dependent panel framework. (deposited 20 May 2022 07:36)
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