How important is innovation? A Bayesian factor-augmented productivity model on panel data

Georges Bresson (1, 2) , Jean-Michel Etienne (3) , Pierre Mohnen (4)

1 TEPP - Travail, Emploi et Politiques Publiques
2 ERMES - Equipe de recherche sur les marches, l'emploi et la simulation
3 UP11 - Université Paris-Sud - Paris 11
4 Maastricht University [Maastricht]

This paper proposes a Bayesian approach to estimate a factor augmented productivity equation. We exploit the panel dimension of our data and distinguish individual-specific and time-specific factors. On the basis of 14 technology and infrastructure indicators from 37 countries over a 10-year period (1998 to 2007), we construct summary indicators of these two components and estimate their e ect on the growth and the international diff erences in GDP per capita.

Mots clés: Bayesian factor-augmented model; innovation; MCMC; panel data; productivity

Domaines: Economies et finances

Georges Bresson, Jean-Michel Etienne, Pierre Mohnen. How important is innovation? A Bayesian factor-augmented productivity model on panel data. 2011. ⟨halshs-00812155⟩