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

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  • Georges Bresson

    (TEPP - Travail, Emploi et Politiques Publiques - CNRS : FR3435 - Université Paris-Est Marne-la-Vallée (UPEMLV), ERMES - Equipe de recherche sur les marches, l'emploi et la simulation - CNRS : UMR7017 - Université Paris II - Panthéon-Assas)

  • Jean-Michel Etienne

    (Université Paris Sud - Université Paris XI - Paris Sud)

  • Pierre Mohnen

    ()
    (Maastricht University - Maastricht University)

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-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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Bibliographic Info

Paper provided by HAL in its series Working Papers with number halshs-00812155.

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Date of creation: May 2011
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Handle: RePEc:hal:wpaper:halshs-00812155

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Related research

Keywords: Bayesian factor-augmented model; innovation; MCMC; panel data; productivity;

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  1. Fagerberg, Jan & Srholec, Martin, 2008. "National innovation systems, capabilities and economic development," Research Policy, Elsevier, vol. 37(9), pages 1417-1435, October.
  2. Jan Fagerberg & Martin Srholec & Bart Verspagen, 2010. "The Role of Innovation in Development," Review of Economics and Institutions, Università di Perugia, vol. 1(2).
  3. Georges Bresson & Cheng Hsiao, 2011. "A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris," AStA Advances in Statistical Analysis, Springer, vol. 95(4), pages 501-529, December.
  4. Bresson G. & Hsiao C. & Pirotte A., 2007. "Assessing the Contribution of R&D to Total Factor Productivity – a Bayesian Approach to Account for Heterogeneity And Heteroscedasticity," Working Papers ERMES 0708, ERMES, University Paris 2.
  5. Emanuel Moench & Serena Ng & Simon Potter, 2009. "Dynamic hierarchical factor models," Staff Reports 412, Federal Reserve Bank of New York.
  6. Hecq, Alain & Palm, Franz C. & Urbain, Jean-Pierre, 2006. "Common cyclical features analysis in VAR models with cointegration," Journal of Econometrics, Elsevier, vol. 132(1), pages 117-141, May.
  7. Pagan, Adrian, 1984. "Econometric Issues in the Analysis of Regressions with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(1), pages 221-47, February.
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