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

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 diff erences in GDP per capita.

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Year of publication:	2011-05
Authors:	Bresson, Georges ; Etienne, Jean-Michel ; Mohnen, Pierre
Institutions:	HAL
Subject:	Bayesian factor-augmented model \| innovation \| MCMC \| panel data \| productivity

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Extent:	application/pdf
Series:	Working Papers.
Type of publication:	Book / Working Paper
Notes:	View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00812155
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010899343