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

Georges BRESSON (), Etienne Jean-Michel and Pierre Mohnen ()

No 2011-06, TEPP Working Papers from TEPP Fédération de Recherche Travail Emploi et Politiques Publiques

Abstract: This paper proposes a Bayesian approach to estimate a factor augmented productivity equation. We exploit the panel dimension of our data and distinguish individual-specic and time-specic 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 di􀀞erences in GDP per capita.

Keywords: Bayesian factor-augmented model; innovation; MCMC; panel data; productivity. (search for similar items in EconPapers)
JEL-codes: C23 C38 O47 (search for similar items in EconPapers)
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Working Paper: How important is innovation? A Bayesian factor-augmented productivity model on panel data (2011)
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