How important is innovation? A Bayesian factor-augmented productivity model on panel data
G. Bresson, J.-M. Etienne & Pierre Mohnen
#2014-052
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 21 technology, infrastructure and institution indicators from 82
countries over a 19-year period (1990 to 2008), we construct summary
indicators of these three components and estimate their effect on the
growth and the international differences in GDP per capita.
Keywords: Bayesian factor-augmented model, innovation, MCMC, panel data,
productivity.
JEL classification: C23, C38, O47
