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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, SEEDS - Sustainability Environmental Economics and Dynamics Studies (Università degli Studi di Ferrara)
Antonio Musolesi: UniFE - Università degli Studi di Ferrara = University of Ferrara, SEEDS - Sustainability Environmental Economics and Dynamics Studies (Università degli Studi di 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 SupAgro - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement

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Abstract: This paper reconsiders the international technology diffusion model. Because the high degree of uncertainty surrounding the Data Generating Process and the likely presence of nonlinearities and latent common factors, it considers alternative nonparametric panel specifications which extend the Common Correlated Effects approach and then contrasts the out-of-sample performance of them with those of more common parametric models. To do so, we extend a recently proposed data-driven model choice approach, which takes its roots on cross validation and aims at testing whether two competing approximate models are equivalent in terms of their expected true error, to the case of cross-sectionally dependent panels, by exploiting moving block bootstrap resampling methods and assessing forecasting performances of competing models. Our results indicate that the adoption of a fully nonparametric specification provides better performances. This work also refines previous results by showing threshold effects, nonlinearities and interactions, which are obscured in parametric specifications and which have relevant implications for policy.

Keywords: Large panels; Cross-sectional dependence; Factor models; Nonparametric regression; Spline functions; Approximate model; Predictive accuracy; Moving block bootstrap; International technology diffusion. (search for similar items in EconPapers)
Date: 2021-05-12
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