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Estimating a Demand System with Seasonally Differenced Data

  • Harri, Ardian
  • Muhammad, Andrew
  • Anderson, John D.

Researchers estimating demand systems have often used annual data even though monthly or quarterly data are available. Monthly data may be avoided because with monthly data it becomes more difficult to specify seasonality, autocorrelation is more likely to be significant, and there is a greater chance of finding significant dynamics in demand. This paper shows how to obtain consistent and asymptotically efficient estimates of a demand system using seasonal differenced data. It also shows that several alternative estimators are either inefficient or implausible for demand systems.

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File URL: http://purl.umn.edu/6427
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Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida with number 6427.

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Date of creation: 2008
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Handle: RePEc:ags:aaea08:6427
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  1. Hylleberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal, Integration And Cointegration," Papers 6-88-2, Pennsylvania State - Department of Economics.
  2. Eales, James S. & Unnevehr, Laurian J., 1994. "The inverse almost ideal demand system," European Economic Review, Elsevier, vol. 38(1), pages 101-115, January.
  3. Seale, James L., Jr. & Merchant, Mary, 2002. "Imports versus Domestic Production: A Demand System Analysis of the U.S. Red Wine Market," Technical Papers 15637, University of Florida, International Agricultural Trade and Policy Center.
  4. Beach, Charles M & MacKinnon, James G, 1979. "Maximum Likelihood Estimation of Singular Equation Systems with Autoregressive Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 20(2), pages 459-64, June.
  5. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September.
  6. James Eales & Catherine Durham & Cathy R. Wessells, 1997. "Generalized Models of Japanese Demand for Fish," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(4), pages 1153-1163.
  7. Andrew Muhammad, 2007. "The impact of increasing non-agricultural market access on EU demand for imported fish: implications for Lake Victoria chilled fillet exports," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 34(4), pages 461-477, December.
  8. Henry L. Bryant & George C. Davis, 2008. "Revisiting Aggregate U.S. Meat Demand with a Bayesian Averaging of Classical Estimates Approach: Do We Need a More General Theory?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(1), pages 103-116.
  9. Carlos Arnade & Daniel Pick & Mark Gehlhar, 2004. "Locating seasonal cycles in demand models," Applied Economics Letters, Taylor & Francis Journals, vol. 11(9), pages 533-535.
  10. Brown, Mark G & Lee, Jonq-Ying & Seale, James L, Jr, 1995. "A Family of Inverse Demand Systems and Choice of Functional Form," Empirical Economics, Springer, vol. 20(3), pages 519-30.
  11. John D. Jackson, 1997. "Effects of Health Information and Generic Advertising on U.S. Meat Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 13-23.
  12. Kinnucan, Henry W. & Chang, Hui-Shung (Christie) & Venkateswaran, Meenakshi, 1993. "Generic Advertising Wearout," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 61(03), December.
  13. Moschini, GianCarlo & Meilke, Karl D., 1989. "Modeling the Pattern of Structural Change in U.S. Meat Demand," Staff General Research Papers 11266, Iowa State University, Department of Economics.
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