Interactive R&D spillovers: an estimation strategy based on forecasting-driven model selection

Georgios Gioldasis, Antonio Musolesi and Michel Simioni ()
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Georgios Gioldasis: UniFE - Università degli Studi di Ferrara = University of Ferrara
Antonio Musolesi: 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

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Abstract: This paper proposes an estimation strategy that exploits recent non-parametric panel data methods that allow for a multifactor error structure and extends a recently proposed datadriven model-selection procedure, which has its roots in cross validation and aims to test whether two competing approximate models are equivalent in terms of their expected true error. We extend this procedure to a large panel data framework by using moving block bootstrap resampling techniques in order to preserve cross-sectional dependence in the bootstrapped samples. Such an estimation strategy is illustrated by revisiting an analysis of international technology diffusion. Model selection procedures clearly conclude in the superiority of a fully non-parametric (non-additive) specification over parametric and even semi-parametric (additive) specifications. This work also refines previous results by showing threshold effects, non-linearities, and interactions that are obscured in parametric specifications and which have relevant implications for policy.

Keywords: large panels; cross-sectional dependence; factor models; non-parametric regression; spline functions; approximate model; predictive accuracy; moving block bootstrap; international technology diffusion (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ore
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Published in International Journal of Forecasting, 2023, 39 (1), pp.144-169. ⟨10.1016/j.ijforecast.2021.09.009⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03476599

DOI: 10.1016/j.ijforecast.2021.09.009

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