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Estimating a Demand System with Seasonally Differenced Data

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  • Harri, Ardian
  • Brorsen, B. Wade
  • Muhammad, Andrew
  • Anderson, John D.

Abstract

Several recent papers have used annual changes and monthly data to estimate demand systems. Such use of overlapping data introduces a moving average error term. This paper shows how to obtain consistent and asymptotically efficient estimates of a demand system using seasonally differenced data. Monte Carlo simulations and an empirical application to the estimation of the U.S. meat demand are used to compare the proposed estimator with alternative estimators. Once the correct estimator is used, there is no advantage to using overlapping data in estimating a demand system.

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Bibliographic Info

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

Volume (Year): 42 (2010)
Issue (Month): 02 (May)
Pages:

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Handle: RePEc:ags:joaaec:90679

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Related research

Keywords: autocorrelation; demand system; Monte Carlo; overlapping data; seasonal differences; Agribusiness; Demand and Price Analysis; Financial Economics; Research Methods/ Statistical Methods; C13; Q11; Q13;

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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. James L. Seale & Mary A. Marchant & Alberto Basso, 2003. "Imports versus Domestic Production: A Demand System Analysis of the U.S. Red Wine Market," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 25(1), pages 187-202.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
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