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Issues and Strategies for Aggregate Supply Response Estimation for Policy Analyses

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  • Ramirez, Octavio A.
  • Mohanty, Samarendu
  • Carpio, Carlos E.
  • Denning, Megan

Abstract

We demonstrate the use of the small-sample econometrics principles and strategies to come up with reliable yield and acreage models for policy analyses. We focus on demonstrating the importance of proper representation of systematic and random components of the model for improving forecasting precision along with more reliable confidence intervals for the forecasts. A probability distribution function modeling approach, which has been shown to provide more reliable confidence intervals for the dependent variable forecasts than the standard models that assume error term normality, is used to estimate cotton supply response in the Southeastern United States.

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Bibliographic Info

Article provided by Southern Agricultural Economics Association in its journal Journal of Agricultural and Applied Economics.

Volume (Year): 36 (2004)
Issue (Month): 02 (August)
Pages:

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Handle: RePEc:ags:joaaec:43420

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Web page: http://www.saea.org/jaae/jaae.htm
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Related research

Keywords: nonnormality; probability distribution function; supply response; Q11; Q18; C32;

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References

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  1. Ramirez, Octavio A. & Fadiga, Mohamadou L., 2003. "Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(01), April.
  2. Ramirez, Octavio A. & Somarriba, Eduardo, 2000. "Risk And Returns Of Diversified Cropping Systems Under Nonnormal, Cross-, And Autocorrelated Commodity Price Structures," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(02), December.
  3. Hal Hill & Budy P. Resosudarmo, 2012. "Introduction," Bulletin of Indonesian Economic Studies, Taylor & Francis Journals, vol. 48(2), pages 129-142, August.
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Cited by:
  1. Capps, Oral, Jr. & Williams, Gary W., 2006. "The Economic Effectiveness of the Cotton Checkoff Program," Reports 90753, Texas A&M University, Agribusiness, Food, and Consumer Economics Research Center.

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