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

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.