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Spatial Pricing Efficiency: The Case Of U.S. Long Grain Rice

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  • Djunaidi, Harjanto
  • Young, Kenneth B.
  • Wailes, Eric J.
  • Hoffman, Linwood A.
  • Childs, Nathan W.

Abstract

The spatial rice price relationships for U.S. long grain rough rice are affected by many factors besides the transportation cost between markets, such as milling, processing, cooking and nutritional value as well as physical characteristics. This study applies a time series framework to analyze long run price relationships for Arkansas, Mississippi, Louisiana, Texas and California long grain rice. Johansen's test results showed that at least there are two cointegrating price vectors. However, such a finding is not supported by the ECM model in any of the price series.

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Bibliographic Info

Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2001 Annual meeting, August 5-8, Chicago, IL with number 20457.

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Date of creation: 2001
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Handle: RePEc:ags:aaea01:20457

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Keywords: Demand and Price Analysis;

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  1. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-65, May.
  2. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
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Cited by:
  1. Van Winkle, Tyler W. & Schroeder, Ted C., 2008. "Spatial Price Discovery, Dynamics, and Leadership in Evolving Distiller’s Grain Markets," 2008 Annual Meeting, February 2-6, 2008, Dallas, Texas 6933, Southern Agricultural Economics Association.
  2. O'Brien, Daniel M., 2009. "The Effects of the Micro-Market Structure for Kansas Grain Elevators on Spatial Grain Price Differentials," 2009 Conference, April 20-21, 2009, St. Louis, Missouri 53041, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.

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