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Modeling Production Risk With A Two-Step Procedure

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  • Asche, Frank
  • Tveteras, Ragnar

Abstract

This study deals with modeling of production risk by means of a two-step procedure. In contrast to earlier studies of production risk, we do not immediately adopt restrictive functional forms for the risky production technology. We first test for the presence of production risk. If production risk is found to be present, the mean and risk functions are estimated separately. This allows the use of more flexible functional forms for both the mean and the risk functions than commonly found in the literature. An empirical application to Norwegian salmon farming, where restrictive specifications of the technology are rejected, demonstrates the validity of our approach. Presence of production risk many primary production sectors implies that this approach should be considered in productivity studies.

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File URL: http://purl.umn.edu/30790
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Bibliographic Info

Article provided by Western Agricultural Economics Association in its journal Journal of Agricultural and Resource Economics.

Volume (Year): 24 (1999)
Issue (Month): 02 (December)
Pages:

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Handle: RePEc:ags:jlaare:30790

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Web page: http://waeaonline.org/
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Keywords: Production Economics;

References

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  1. Godfrey,L. G., 1991. "Misspecification Tests in Econometrics," Cambridge Books, Cambridge University Press, number 9780521424592, October.
  2. Gourieroux Christian & Monfort Alain & Trognon A, 1981. "Pseudo maximum likelihood methods : theory," CEPREMAP Working Papers (Couverture Orange) 8129, CEPREMAP.
  3. T. Bjørndal & K. G. Salvanes, 1995. "Gains From Deregulation? An Empirical Test For Efficiency Gains In The Norwegian Fish Farming Industry," Journal of Agricultural Economics, Wiley Blackwell, vol. 46(1), pages 113-126.
  4. G. H. Wan & J. R. Anderson, 1990. "Estimating Risk Effects In Chinese Foodgrain Production," Journal of Agricultural Economics, Wiley Blackwell, vol. 41(1), pages 85-93.
  5. Just, Richard E. & Pope, Rulon D., 1978. "Stochastic specification of production functions and economic implications," Journal of Econometrics, Elsevier, vol. 7(1), pages 67-86, February.
  6. Arne Hallam & Rashid M. Hassan & B. D'Silva, 1989. "Measuring Stochastic Technology for the Multi-product Firm: The Irrigated Farms of Sudan," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 37(3), pages 495-512, November.
  7. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-46, March.
  8. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-94, September.
  9. Hurd, Brian H., 1994. "Yield Response And Production Risk: An Analysis Of Integrated Pest Management In Cotton," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(02), December.
  10. Wan, Guang H & Griffiths, William E & Anderson, Jock R, 1992. "Using Panel Data to Estimate Risk Effects in Seemingly Unrelated Production Functions," Empirical Economics, Springer, vol. 17(1), pages 35-49.
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Citations

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Cited by:
  1. Murat Isik & Madhu Khanna, 2003. "Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 305-317.
  2. 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).
  3. Tomasz Gerard Czekaj & Arne Henningsen, 2013. "Panel Data Nonparametric Estimation of Production Risk and Risk Preferences: An Application to Polish Dairy Farms," IFRO Working Paper 2013/6, University of Copenhagen, Department of Food and Resource Economics.
  4. Kristin Roll, 2013. "Measuring performance, development and growth when restricting flexibility," Journal of Productivity Analysis, Springer, vol. 39(1), pages 15-25, February.
  5. Howard, Donald D. & English, Burton C. & Larson, James A. & Roberts, Roland K. & Walters, Jeremy T., 2004. "Effects Of Risk, Disease, And Nitrogen Source On Optimal Nitrogen Fertilization Rates In Winter Wheat Production," 2004 Annual Meeting, February 14-18, 2004, Tulsa, Oklahoma 34688, Southern Agricultural Economics Association.
  6. Roberts, Roland K. & Walters, Jeremy T. & Larson, James A. & English, Burton C. & Howard, Donald D., 2004. "Optimal Nitrogen Fertilization Rates in Winter Wheat Production as Affected by Risk, Disease, and Nitrogen Source," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 36(01), April.
  7. Jaenicke, Edward C. & Frechette, Darren L. & Larson, James A., 2003. "Estimating Production Risk and Inefficiency Simultaneously: An Application to Cotton Cropping Systems," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(03), December.
  8. Asche, Frank & Guttormsen, Atle G. & Roll, Kristin H., 2006. "Modelling Production Risk in Small Scale Subsistence Agriculture," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25574, International Association of Agricultural Economists.
  9. Tveteras, Ragnar & Flaten, Ola & Lien, Gudbrand D., 2008. "Production risk in multi-output industries: estimates from Norwegian dairy farms," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 43958, European Association of Agricultural Economists.
  10. Straume, Hans-Martin, 2013. "Currency invoicing in Norwegian salmon export," Working Papers in Economics 11/13, University of Bergen, Department of Economics.

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