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A Translog Cost Function Analysis Of U.S. Agriculture: A Dynamic Specification

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  • Moss, Charles B.
  • Erickson, Kenneth W.
  • Ball, V. Eldon
  • Mishra, Ashok K.

Abstract

This study has used an empirical approach developed by Urga and Walters (2003) to examine the implications of the short-run specification of the standard translog cost specification along with the possible implications of non-stationarity. We have estimated a dynamic translog cost specification complete with dynamic share equations for U.S. agriculture and compared it to the static, long-run specification. We found that the dynamic translog specification yielded more significant parameter estimates, and yielded results that are consistent with economic theory. In particular, the coefficient m (the adjustment cost parameter) determines the overall autoregressive structure of the model. The fact that its estimated value (0.36) is statistically different from zero at any conventional level of confidence indicates that the dynamic structure of the model is important. This finding illustrates the superiority of the short-run, dynamic specification over the static, long-run model.

Suggested Citation

  • Moss, Charles B. & Erickson, Kenneth W. & Ball, V. Eldon & Mishra, Ashok K., 2003. "A Translog Cost Function Analysis Of U.S. Agriculture: A Dynamic Specification," 2003 Annual meeting, July 27-30, Montreal, Canada 22027, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea03:22027
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    File URL: http://purl.umn.edu/22027
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    References listed on IDEAS

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    1. Hongil Lim & C. Richard Shumway, 1997. "Technical Change and Model Specification: U.S. Agricultural Production," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 543-554.
    2. J. Stephen Clark & Curtis E. Youngblood, 1992. "Estimating Duality Models with Biased Technical Change: A Time Series Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 74(2), pages 353-360.
    3. Susan M. Capalbo & Michael Denny, 1985. "Testing Long-Run Productivity Models for the Canadian and U.S. Agricultural Sectors," NBER Working Papers 1764, National Bureau of Economic Research, Inc.
    4. Anderson, G J & Blundell, R W, 1982. "Estimation and Hypothesis Testing in Dynamic Singular Equation Systems," Econometrica, Econometric Society, vol. 50(6), pages 1559-1571, November.
    5. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    6. David K. Lambert & J.S. Shonkwiler, 1995. "Factor Bias under Stochastic Technical Change," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(3), pages 578-590.
    7. Diewert, Walter E & Wales, Terence J, 1987. "Flexible Functional Forms and Global Curvature Conditions," Econometrica, Econometric Society, vol. 55(1), pages 43-68, January.
    8. Huffman, Wallace E. & Ball, E. & Gopinath, M. & Somwaru, A., 2002. "Public R&D and Infrastructure Policies: Effects on Cost of Midwestern Agriculture," Staff General Research Papers Archive 10431, Iowa State University, Department of Economics.
    9. Robert G. Chambers & Utpal Vasavada, 1983. "Testing Asset Fixity for U.S. Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 65(4), pages 761-769.
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    Cited by:

    1. Khayyat, Nabaz T. & Heshmati, Almas, 2014. "Production Risk, Energy Use Efficiency and Productivity of Korean Industries," IZA Discussion Papers 8081, Institute for the Study of Labor (IZA).

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    Keywords

    Agribusiness;

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