How important is innovation? A Bayesian factor-augmented productivity model on panel data
AbstractThis paper proposes a Bayesian approach to estimate a factor augmented productivity equation. We exploit the panel dimension of our data and distinguish individual-specic and time-specic 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.
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Date of creation: May 2011
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Bayesian factor-augmented model; innovation; MCMC; panel data; productivity;
Other versions of this item:
- Georges Bresson & Jean-Michel Etienne & Pierre Mohnen, 2011. "How important is innovation? A Bayesian factor-augmented productivity model on panel data," TEPP Working Paper 2011-06, TEPP.
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