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

Georges Bresson, Jean-Michel Etienne () and Pierre Mohnen
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Jean-Michel Etienne: UP11 - Université Paris-Sud - Paris 11

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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-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.

Keywords: Bayesian factor-augmented model; innovation; MCMC; panel data; productivity (search for similar items in EconPapers)
Date: 2011-05
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00812155v1
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