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An application of maximum entropy estimation: the demand for meat in the United Kingdom

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  • Iain Fraser

Abstract

In many econometric studies of demand relationships the design matrix is frequently subject to severe collinearity. In this paper the Generalized Maximum Entropy methodology is introduced and used to estimate a set of demand relationships. The ability of Generalized Maximum Entropy to estimate economic relationships that are typically subject to a high degree of collinearity among the explanatory variables, thus potentially causing traditional methods of estimation to be unreliable, is explained. The results derived by this alternative method of estimation, for a UK meat demand data set, are analysed and examined. The potential for this emerging estimation methodology is discussed.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics.

Volume (Year): 32 (2000)
Issue (Month): 1 ()
Pages: 45-59

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Handle: RePEc:taf:applec:v:32:y:2000:i:1:p:45-59

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Cited by:
  1. Nicola Branson, 2009. "Re-weighting the OHS and LFS National household Survey Data to create a consistent series over time: A Cross Entropy Estimation Approach," SALDRU Working Papers 38, Southern Africa Labour and Development Research Unit, University of Cape Town.
  2. Marsh, Thomas L. & Mittelhammer, Ronald C., 2001. "Adaptive Truncated Estimaton Applied To Maximum Entropy," 2001 Annual Meeting, July 8-11, 2001, Logan, Utah 36169, Western Agricultural Economics Association.
  3. Esteban Fernandez-Vazquez, 2011. "Estimating spatial weighting matrices in cross-regressive models by entropy techniques," ERSA conference papers ersa10p503, European Regional Science Association.
  4. Campbell, Randall C. & Hill, R. Carter, 2005. "A Monte Carlo study of the effect of design characteristics on the inequality restricted maximum entropy estimator," Review of Applied Economics, Review of Applied Economics, vol. 1(1).
  5. Matias Mayor Fernandez & Esteban Fernandez Vazquez & Jorge Rodriguez Valez, 2006. "Spatial Structures and Spatial Spillovers: A GME Approach," ERSA conference papers ersa06p777, European Regional Science Association.
  6. Rui Fragoso & Maria Leonor da Silva Carvalho, 2013. "Estimation of cost allocation coefficients at the farm level using an entropy approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 1893-1906, September.
  7. Nganou, Jean-Pascal, 2005. "Estimates of Armington parameters for a landlocked economy," MPRA Paper 31459, University Library of Munich, Germany, revised 10 Aug 2008.
  8. R. Carter Hill & Randall C. Campbell, . "Maximum Entropy Estimation in Economic Models with Linear Inequality Restrictions," Departmental Working Papers 2001-11, Department of Economics, Louisiana State University.
  9. Qiuqiong Huang & Richard Howitt & Scott Rozelle, 2012. "Estimating production technology for policy analysis: trading off precision and heterogeneity," Journal of Productivity Analysis, Springer, vol. 38(2), pages 219-233, October.
  10. Fernández Vázquez, Esteban, 2011. "Updating weighting matrices by Cross-Entropy," Investigaciones Regionales, Asociación Española de Ciencia Regional, issue 21, pages 53-69.
  11. Fernández Vázquez, Esteban & Los, Bart, 2007. "A Maximum Entropy Approach to the Indenitication of Productive Technology Spillovers," Discussion Papers 1106, The Research Institute of the Finnish Economy.
  12. repec:lrk:lrkwkp:fiirs016 is not listed on IDEAS
  13. Rezek, Jon P. & Campbell, Randall C., 2007. "Cost estimates for multiple pollutants: A maximum entropy approach," Energy Economics, Elsevier, vol. 29(3), pages 503-519, May.

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