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Incorporating temporal and country heterogeneity in growth accounting—an application to EU-KLEMS

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  • A. Peyrache

    (The University of Queensland)

  • A. N. Rambaldi

    (The University of Queensland)

Abstract

The paper derives measures of sectoral productivity from a model specification that allows for cross-sectional specific trends and time varying slopes in panel models with fixed N. The specification nests a number of commonly used panel data models introduced in the literature which deal with group specific trends. The econometric model is represented in state-space form. We provide a production frontier interpretation of this group specific temporal variation and derive a post-estimation growth accounting to provide a quantitative assessment of the main factors behind sectoral labour productivity growth. We make use of the EU-KLEMS dataset, covering the period 1977–2007 for 13 countries and 20 sectors of each economy.

Suggested Citation

  • A. Peyrache & A. N. Rambaldi, 2017. "Incorporating temporal and country heterogeneity in growth accounting—an application to EU-KLEMS," Journal of Productivity Analysis, Springer, vol. 47(2), pages 143-166, April.
    • Handle: RePEc:kap:jproda:v:47:y:2017:i:2:d:10.1007_s11123-017-0498-2
    DOI: 10.1007/s11123-017-0498-2
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    References listed on IDEAS

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    1. Evangelia Desli & Subhash Ray & Subal Kumbhakar, 2003. "A dynamic stochastic frontier production model with time-varying efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 10(10), pages 623-626.
    2. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    3. Giannis Karagiannis & Vangelis Tzouvelekas, 2009. "Measuring technical efficiency in the stochastic varying coefficient frontier model," Agricultural Economics, International Association of Agricultural Economists, vol. 40(4), pages 389-396, July.
    4. Kneip, Alois & Sickles, Robin C. & Song, Wonho, 2012. "A New Panel Data Treatment For Heterogeneity In Time Trends," Econometric Theory, Cambridge University Press, vol. 28(3), pages 590-628, June.
    5. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    6. 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, vol. 119(538), pages 374-403, June.
    7. Harvey, Andrew, 2006. "Forecasting with Unobserved Components Time Series Models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 7, pages 327-412, Elsevier.
    8. Sickles, Robin C., 2005. "Panel estimators and the identification of firm-specific efficiency levels in parametric, semiparametric and nonparametric settings," Journal of Econometrics, Elsevier, vol. 126(2), pages 305-334, June.
    9. Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
    10. 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, vol. 35(1), pages 231-243, February.
    11. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    12. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    13. Luis Orea, 2002. "Parametric Decomposition of a Generalized Malmquist Productivity Index," Journal of Productivity Analysis, Springer, vol. 18(1), pages 5-22, July.
    14. 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, vol. 38(3), pages 387-399, July.
    15. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676.
    16. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    17. 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., vol. 15(3), pages 321-333.
    18. Pavlos Almanidis & Giannis Karagiannis & Robin Sickles, 2015. "Semi-nonparametric spline modifications to the Cornwell–Schmidt–Sickles estimator: an analysis of US banking productivity," Empirical Economics, Springer, vol. 48(1), pages 169-191, February.
    19. Jin, Hui & Jorgenson, Dale W., 2010. "Econometric modeling of technical change," Journal of Econometrics, Elsevier, vol. 157(2), pages 205-219, August.
    20. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    21. Subal C. Kumbhakar, 2004. "Productivity and technical change: Measurement and testing," Empirical Economics, Springer, vol. 29(1), pages 185-191, January.
    22. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    23. 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, vol. 19(2-3), pages 233-238, August.
    24. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
    25. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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