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A State-Space Stochastic Frontier Panel Data Model

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  • A. Peyrache

    ()
    (CEPA - School of Economics, The University of Queensland)

  • A. N. Rambaldi

    ()
    (CEPA - School of Economics, The University of Queensland)

Abstract

In this paper we introduce a state-space approach to the econometric modelling of cross-sectional specific trends (temporal variation in individual heterogeneity) and time varying slopes in the context of panel data regressions. We show that our state-space panel stochastic frontier model nests some of the popular models proposed in the literature on stochastic frontier to accommodate time varying inefficiency and its dynamic version (productivity). A detailed discussion of alternative model specifications is provided and estimation (along with testing procedures for model selection) is presented. The empirical application uses the EU-KLEMS dataset which provides data in the period 1977-2007 for 13 countries and 20 sectors of each economy. Our main empirical interest is centered on productivity analysis and thus we focus on the stochastic frontier interpretation of this cross-sectional specific temporal variation. A post-estimation growth accounting is introduced in order to provide a quantitative assessment of the main factors behind sectoral labour productivity growth for each country.

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Bibliographic Info

Paper provided by School of Economics, University of Queensland, Australia in its series CEPA Working Papers Series with number WP012012.

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Date of creation: Apr 2012
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Handle: RePEc:qld:uqcepa:77

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  1. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, Elsevier, vol. 6(1), pages 21-37, July.
  2. Evangelia Desli & Subhash Ray & Subal Kumbhakar, 2003. "A dynamic stochastic frontier production model with time-varying efficiency," Applied Economics Letters, Taylor & Francis Journals, Taylor & Francis Journals, vol. 10(10), pages 623-626.
  3. Subal C. Kumbhakar, 2004. "Productivity and technical change: Measurement and testing," Empirical Economics, Springer, Springer, vol. 29(1), pages 185-191, January.
  4. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, Elsevier, vol. 19(2-3), pages 233-238, August.
  5. Mary O'Mahony & Marcel P. Timmer, 2009. "Output, Input and Productivity Measures at the Industry Level: The EU KLEMS Database," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 119(538), pages F374-F403, 06.
  6. Pizzinga, Adrian & Fernandes, Cristiano & Contreras, Sergio, 2008. "Restricted Kalman filtering revisited," Journal of Econometrics, Elsevier, Elsevier, vol. 144(2), pages 428-429, June.
  7. Doran, Howard E. & Rambaldi, Alicia N., 1997. "Applying linear time-varying constraints to econometric models: With an application to demand systems," Journal of Econometrics, Elsevier, Elsevier, vol. 79(1), pages 83-95, July.
  8. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, Elsevier, vol. 38(3), pages 387-399, July.
  9. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, Elsevier, vol. 46(1-2), pages 185-200.
  10. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 21(5), pages 669-676.
  11. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, Elsevier, vol. 46(1-2), pages 201-211.
  12. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 2(4), pages 367-74, October.
  13. Atkinson, Scott E & Cornwell, Christopher, 1994. "Parametric Estimation of Technical and Allocative Inefficiency with Panel Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(1), pages 231-43, February.
  14. Luis Orea, 2002. "Parametric Decomposition of a Generalized Malmquist Productivity Index," Journal of Productivity Analysis, Springer, Springer, vol. 18(1), pages 5-22, July.
  15. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, Elsevier, vol. 4(2), pages 115-145, May.
  16. Tim Coelli & Sanzidur Rahman & Colin Thirtle, 2003. "A stochastic frontier approach to total factor productivity measurement in Bangladesh crop agriculture, 1961-92," Journal of International Development, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 15(3), pages 321-333.
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