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Estimation of firm-specific technological bias, technical change and total factor productivity growth: a dual approach


  • Subal Kumbhakar
  • Shinichiro Nakamura
  • Almas Heshmati


This paper deals with modeling firm-specific technical change (TC), and technological biases (inputs and scale) in estimating total factor productivity (TFP) growth. Several dual parametric econometric models are used for this purpose. We examine robustness of TFP growth and TC among competing models. These models include the traditional time trend (TT) model and the general index (GI) model. The TT and the GI models are generalized to accommodate firm-specific TC and technological bias (in inputs and output). Both nested and non-nested tests are used to select the appropriate models. Firm-level panel data from the Japanese chemical industry during 1968- 1987 is used as an application.

Suggested Citation

  • Subal Kumbhakar & Shinichiro Nakamura & Almas Heshmati, 2000. "Estimation of firm-specific technological bias, technical change and total factor productivity growth: a dual approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 162-173.
  • Handle: RePEc:taf:emetrv:v:19:y:2000:i:4:p:162-173 DOI: 10.1080/07474930008800483

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    References listed on IDEAS

    1. Stephen Bond & Anke Hoeffler & Jonathan Temple, 2001. "GMM Estimation of Empirical Growth Models," Economics Papers 2001-W21, Economics Group, Nuffield College, University of Oxford.
    2. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    3. 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.
    4. Nelson, Charles R & Startz, Richard, 1990. "Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," Econometrica, Econometric Society, vol. 58(4), pages 967-976, July.
    5. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    6. Nelson, Charles R & Startz, Richard, 1990. "The Distribution of the Instrumental Variables Estimator and Its t-Ratio When the Instrument Is a Poor One," The Journal of Business, University of Chicago Press, vol. 63(1), pages 125-140, January.
    7. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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    Cited by:

    1. Almas Heshmati & Subal C. Kumbhakar, 2014. "A general model of technical change with an application to the OECD countries," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 23(1), pages 25-48, January.
    2. Sauer, Johannes & Graversen, Jesper T. & Park, Timothy A., 2006. "Breathtaking or Stagnating? - Productivity, Technical Change and Structural Dynamics in Danish Organic Farming," 2006 Annual meeting, July 23-26, Long Beach, CA 21481, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Heshmati, Almas, 2002. "Productivity measurement in Swedish departments of gynecology and obstetrics," Structural Change and Economic Dynamics, Elsevier, vol. 13(3), pages 315-336, September.
    4. Chia-Hung Sun, 2005. "Productivity growth in East Asian manufacturing: a fading miracle or measurement problem?," Journal of International Development, John Wiley & Sons, Ltd., vol. 17(1), pages 1-19.
    5. Kumbhakar, Subal C., 2002. "Decomposition of technical change into input-specific components: a factor augmenting approach," Japan and the World Economy, Elsevier, vol. 14(3), pages 243-264, August.


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