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Why are estimates of agricultural supply response so variable?

  • Diebold, Francis X.
  • Lamb, Russell L.

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.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 76 (1997)
Issue (Month): 1-2 ()
Pages: 357-373

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Handle: RePEc:eee:econom:v:76:y:1997:i:1-2:p:357-373
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  1. Zellner, Arnold & Geisel, Martin S, 1970. "Analysis of Distributed Lag Models with Application to Consumption Function Estimation," Econometrica, Econometric Society, vol. 38(6), pages 865-88, November.
  2. Askari, Hossein & Cummings, John Thomas, 1977. "Estimating Agricultural Supply Response with the Nerlove Model: A Survey," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 257-92, June.
  3. Braulke, Michael, 1982. "A Note on the Nerlove Model of Agricultural Supply Response," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 23(1), pages 241-44, February.
  4. Zaman, Asad, 1981. "Estimators without moments : The case of the reciprocal of a normal mean," Journal of Econometrics, Elsevier, vol. 15(2), pages 289-298, February.
  5. Just, Richard E., 1993. "Discovering Production and Supply Relationships: Present Status and Future Opportunities," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 61(01), April.
  6. Park, Soo-Bin, 1982. "Some sampling properties of minimum expected loss (MELO) estimators of structural coefficients," Journal of Econometrics, Elsevier, vol. 18(3), pages 295-311, April.
  7. Fomby, Thomas B. & Guilkey, David K., 1978. "On choosing the optimal level of significance for the Durbin-Watson test and the Bayesian alternative," Journal of Econometrics, Elsevier, vol. 8(2), pages 203-213, October.
  8. Tomek, William G. & Myers, Robert J., 1993. "Empirical Analysis Of Agricultural Commodity Prices: A Viewpoint," Working Papers 6847, Cornell University, Department of Applied Economics and Management.
  9. Bewley, R. & Fiebig, D.G., 1989. "Why Are Long-Run Parameter Estimates So Disparate?," Papers 89-3, New South Wales - School of Economics.
  10. 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.
  11. Marc Nerlove, 1979. "The Dynamics of Supply: Retrospect and Prospect," Discussion Papers 394, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  12. Zellner, Arnold, 1985. "Bayesian Econometrics," Econometrica, Econometric Society, vol. 53(2), pages 253-69, March.
  13. Zellner, Arnold, 1978. "Estimation of functions of population means and regression coefficients including structural coefficients : A minimum expected loss (MELO) approach," Journal of Econometrics, Elsevier, vol. 8(2), pages 127-158, October.
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