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Dynamic Factor Demand Models and Productivity Analysis

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  • Nadiri, M.I.
  • Prucha, I.

Abstract

In this paper we discuss recent advances in modeling and estimating dynamic factor demand models, and review the use of such models in analyzing the production structure, the determinants of variable and quasi-fixed factors, and productivity growth. The paper also discusses the traditional approach to productivity analysis based on the Divisia index number methodology. Both approaches may be seen a complementary. The conventional index number approach will measure the rate of technical change correctly if certain assumptions about the underlying technology of the firm and output and input markets hold. Furthermore, the conventional index number approach is appealing in that it can be easily implemented. However, if the underlying assumptions do not hold, then the conventional index number approach will, in general, yield biased estimates of technical change. The econometric approach based on general dynamic factor demand models allows for a careful testing of various features of a postulated model. Furthermore it not only provides for estimates of technical change, but can also yield a rich set of critical information on the structure of production, the dynamics of investment in physical and R&D capital, the effects of spillovers, the depreciation rate of capital, the impact of taxes, expectations, etc. The paper also explores in terms of a Monte Carlo study how estimates of important characteristics of the production process can be affected by model misspecification. The results suggest adopting a simple specification for reasons of convenience may result in serious biases.
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  • Nadiri, M.I. & Prucha, I., 1999. "Dynamic Factor Demand Models and Productivity Analysis," Working Papers 99-09, C.V. Starr Center for Applied Economics, New York University.
  • Handle: RePEc:cvs:starer:99-09
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    Cited by:

    1. Khayyat, Nabaz T. & Lee, Jongsu & Lee, Jeong-Dong, 2014. "How ICT Investment Influences Energy Demand in South Korea and Japan?," MPRA Paper 55454, University Library of Munich, Germany.
    2. Chen, Baoline & Zadrozny, Peter A., 2009. "Estimated U.S. manufacturing production capital and technology based on an estimated dynamic structural economic model," Journal of Economic Dynamics and Control, Elsevier, pages 1398-1418.
    3. Khayyat, Nabaz T. & Lee, Jongsu & Heshmati, Almas, 2014. "How ICT Investment and Energy Use Influence the Productivity of Korean Industries," IZA Discussion Papers 8080, Institute for the Study of Labor (IZA).
    4. Susanto Basu & John Fernald, 2001. "Why Is Productivity Procyclical? Why Do We Care?," NBER Chapters,in: New Developments in Productivity Analysis, pages 225-302 National Bureau of Economic Research, Inc.
    5. Basu, Susanto & Fernald, John G. & Shapiro, Matthew D., 2001. "Productivity growth in the 1990s: technology, utilization, or adjustment?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, pages 117-165.
    6. Petri Rouvinen, 2002. "The existence of R&D spillovers: A cost function estimation with random coefficients," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 11(6), pages 525-541.
    7. Giannis Karagiannis, 2008. "Commentary," Journal of Productivity Analysis, Springer, vol. 30(1), pages 67-68, August.
    8. Elias Kourliouros & George Korres & Emmanuel Marmaras & George Tsobanoglou, 2006. "Economic Geography and Regional Growth: An Empirical Evidence From Greece," ERSA conference papers ersa06p30, European Regional Science Association.
    9. Fuentes Castro, Daniel, 2011. "Productividad y equidad en la economía española desde una perspectiva internacional (1995 – 2009)
      [Productivity and equity in the Spanish economy from an international perspective (1995-2009)]
      ," MPRA Paper 51611, University Library of Munich, Germany.
    10. Diana Tsai & Marc Lin, 2005. "Industrial and Spatial Spillovers and Productivity Growth: Evidence from Taiwan High-Technology Plant Level Data," Journal of Productivity Analysis, Springer, vol. 23(1), pages 109-129, January.
    11. Jeffrey Bernstein & Theofanis Mamuneas, 2008. "Public infrastructure, input efficiency and productivity growth in the Canadian food processing industry," Journal of Productivity Analysis, Springer, vol. 29(1), pages 1-13, February.
    12. Wojciech Szewczyk & Anna Sabadash, 2013. "Macroeconomic Modelling of Public Expenditures on Research and Development in Information and Communication Technologies," JRC Working Papers JRC82943, Joint Research Centre (Seville site).
    13. David Greenstreet, 2007. "Exploiting Sequential Learning to Estimate Establishment-Level Productivity Dynamics and Decision Rules," Economics Series Working Papers 345, University of Oxford, Department of Economics.
    14. Bernstein, Jeffrey I. & Mamuneas, Theofanis P., 2006. "R&D depreciation, stocks, user costs and productivity growth for US R&D intensive industries," Structural Change and Economic Dynamics, Elsevier, vol. 17(1), pages 70-98, January.
    15. Hulten, Charles R., 2010. "Growth Accounting," Handbook of the Economics of Innovation, Elsevier.

    More about this item

    Keywords

    PRODUCTIVITY GROWTH; DYNAMIC FACTOR DEMAND; SPLILLOVER; R&D; TAX INCENTIIVES; CAPITAL UTILIZATION; DEPRECIATION RATE; MISSPECIFICATION TEST.;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • D2 - Microeconomics - - Production and Organizations
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity

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