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Why are estimates of agricultural supply response so variable?

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  • Francis X. Diebold
  • Russell L. Lamb

Abstract

Estimates of the response of agricultural supply to movements in expected price display curiously large variation across crops, regions, and time periods. We argue that this anomaly may be traced, at least in part, to the statistical properties of the commonly-used econometric estimator, which has infinite moments of all orders and may have a bimodal distribution. We propose an alternative minimum-expected-loss estimator, establish its improved sampling properties, and argue for its usefulness in the empirical analysis of agricultural supply response.

Suggested Citation

  • Francis X. Diebold & Russell L. Lamb, 1996. "Why are estimates of agricultural supply response so variable?," Finance and Economics Discussion Series 96-8, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:96-8
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    References listed on IDEAS

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    Cited by:

    1. Keeney, Roman & Hertel, Thomas W., 2008. "Yield Response To Prices: Implications For Policy Modeling," Working papers 45969, Purdue University, Department of Agricultural Economics.
    2. Stigler, Matthieu M., 2018. "Supply response at the field-level: disentangling area and yield effects," 2018 Annual Meeting, August 5-7, Washington, D.C. 274343, Agricultural and Applied Economics Association.
    3. Kleibergen, Frank & Zivot, Eric, 2003. "Bayesian and classical approaches to instrumental variable regression," Journal of Econometrics, Elsevier, vol. 114(1), pages 29-72, May.
    4. Amina Al Naabi & Shekar Bose, 2020. "Do Regulatory Measures Necessarily Affect Oman’s Seafood Export-Supply?," SAGE Open, , vol. 10(3), pages 21582440209, August.
    5. Arnold Zellner, 2003. "Some Recent Developments in Econometric Inference," Econometric Reviews, Taylor & Francis Journals, vol. 22(2), pages 203-215.
    6. Lamb, Russell L., 2000. "Food crops, exports, and the short-run policy response of agriculture in Africa," Agricultural Economics, Blackwell, vol. 22(3), pages 271-298, April.
    7. Traoré, Fousseini, 2013. "Estimating the supply elasticity of cotton in Mali with the Nerlove Model: A bayesian method of moments approach," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement (RAEStud), Institut National de la Recherche Agronomique (INRA), vol. 94(3).
    8. Shen, Edward Z. & Perloff, Jeffrey M., 2001. "Maximum entropy and Bayesian approaches to the ratio problem," Journal of Econometrics, Elsevier, vol. 104(2), pages 289-313, September.
    9. Demont, Matty & Tollens, Eric, 2001. "Uncertainties Of Estimating The Welfare Effects Of Agricultural Biotechnology In The European Union," Working Papers 31828, Katholieke Universiteit Leuven, Centre for Agricultural and Food Economics.
    10. Kim, Jae H. & Fraser, Iain & Hyndman, Rob J., 2011. "Improved interval estimation of long run response from a dynamic linear model: A highest density region approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2477-2489, August.
    11. Roman Keeney & Thomas W. Hertel, 2008. "U.S. Market Potential For Dried Distillers Grain With Solubles," Working Papers 08-13, Purdue University, College of Agriculture, Department of Agricultural Economics.
    12. Hee Mok Park & Puneet Manchanda, 2015. "When Harry Bet with Sally: An Empirical Analysis of Multiple Peer Effects in Casino Gambling Behavior," Marketing Science, INFORMS, vol. 34(2), pages 179-194, March.
    13. Psaradakis, Zacharias & Sola, Martin, 1998. "Finite-sample properties of the maximum likelihood estimator in autoregressive models with Markov switching," Journal of Econometrics, Elsevier, vol. 86(2), pages 369-386, June.
    14. Anbes Tenaye, 2020. "New Evidence Using a Dynamic Panel Data Approach: Cereal Supply Response in Smallholder Agriculture in Ethiopia," Economies, MDPI, vol. 8(3), pages 1-24, July.
    15. Zellner, Arnold & Tobias, Justin, 2001. "Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(1), pages 121-140, February.
    16. Ball, V. Eldon & Moss, Charles B. & Erickson, Kenneth W. & Nehring, Richard F., 2003. "Modeling Supply Response In A Multiproduct Framework Revisited: The Nexus Of Empirics And Economics," 2003 Annual meeting, July 27-30, Montreal, Canada 21981, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Zellner, Arnold, 1998. "The finite sample properties of simultaneous equations' estimates and estimators Bayesian and non-Bayesian approaches," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 185-212.
    18. Andres Ramirez-Hassan & Manuel Correa-Giraldo, 2018. "Focused econometric estimation for noisy and small datasets: A Bayesian Minimum Expected Loss estimator approach," Papers 1809.06996, arXiv.org.

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