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GMM estimation of a production function with panel data : an application to Spanish manufacturing firms

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Abstract

In this paper we consider the estimation of a Cobb-Douglas production function using a panel dataset of Spanish manufacturing firms. As it is stressed in the econometric literature, the use of standard GMM first differences estimators to eliminate the unobserved firm-specific effects may yield imprecise estimates, particularly in the case of the estimation of the production function. The reason is that the high persistence of output and inputs involved in the estimation of production functions make that their lagged levels to be weak instruments for the first differences of these series. The extended GMM estimator proposed by Arellano and Bover (1995) considers further orthogonality conditions based on lagged differences as instruments for the equation in levels. This approach has been applied to the estimation of technological parameters by Blundell and Bond (1999). Our estimation results, based on this approach, confirm the better performance of the extended GMM estimator compared to the standard first-differenced GMM estimator

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  • Sánchez Mangas, Rocío, 2001. "GMM estimation of a production function with panel data : an application to Spanish manufacturing firms," DES - Working Papers. Statistics and Econometrics. WS ws015527, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws015527
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    1. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    2. Jacques Mairesse & Bronwyn H. Hall & Benoît Mulkay, 1999. "Firm-Level Investment in France and the United States: An Exploration of What We Have Learned in Twenty Years," Annals of Economics and Statistics, GENES, issue 55-56, pages 27-67.
    3. Richard Blundell & Stephen Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
    4. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
    5. repec:adr:anecst:y:1999:i:55-56:p:02 is not listed on IDEAS
    6. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    Cited by:

    1. Carmine Ornaghi, 2006. "Assessing the effects of measurement errors on the estimation of production functions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 879-891.
    2. Stucchi, Rodolfo, 2008. "Catching up in total factor productivity through the business cycle : evidence from Spanish manufacturing surveys," UC3M Working papers. Economics we085125, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Sánchez Mangas, Rocío, 2002. "Pseudo-maximum likelihood estimation of a dynamic structural investment model," DES - Working Papers. Statistics and Econometrics. WS ws026218, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Housung Jung & Hyeog Ug Kwon, 2007. "An Alternative System GMM Estimation in Dynamic Panel Models," Hi-Stat Discussion Paper Series d07-217, Institute of Economic Research, Hitotsubashi University.
    5. Aguirregabiria, Victor, 2009. "Econometric Issues and Methods in the Estimation of Production Functions," MPRA Paper 15973, University Library of Munich, Germany.
    6. Sánchez Mangas, Rocío, 2001. "Estimation of a dynamic discrete choice model of irreversible investment," DES - Working Papers. Statistics and Econometrics. WS ws015628, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Giuliana Battisti & Amid-George Mourani & Paul Stoneman, 2010. "Causality and a firm-level innovation scoreboard," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 19(1), pages 7-26.

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