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

Working Paper #2014-052

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

G. Bresson, J.-M. Etienne & Pierre Mohnen #2014-052 PDF

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 codes: C23

C38

O47

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APA 7th ed.

Bresson, G., Etienne, J.-M., & Mohnen, P. (2014). How important is innovation? A Bayesian factor-augmented productivity model on panel data (UNU-MERIT Working Paper No. 2014-052). UNU-MERIT. https://unu-merit.nl/publications/working-papers/abstract/?id=5494

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