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Estimation of Cost Allocation Coefficients at the Farm Level Using an Entropy Approach

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  • Rui Fragoso

    ()
    (University of Évora, Department of Management, CEFAGE/ICAAM)

  • Maria Leonor Carvalho

    ()
    (University of Évora, Department of Economics, ICAAM/CEFAGE)

Abstract

This paper aims to estimate the farm cost allocation coefficients from whole farm input costs. An entropy approach was developed under a Tobit formulation and was applied to a sample of farms from the 2004 FADN data base for Alentejo region, Southern Portugal. A Generalized Maximum Entropy model and Cross Generalized Entropy model were developed to the sample conditions and were tested. Model results were assessed in terms of their precision and estimation power and were compared with observed data. The entropy approach showed to be a flexible and valid tool to estimate incomplete information, namely regarding farm costs.

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Bibliographic Info

Paper provided by University of Evora, CEFAGE-UE (Portugal) in its series CEFAGE-UE Working Papers with number 2011_21.

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Length: 21 pages
Date of creation: 2011
Date of revision:
Handle: RePEc:cfe:wpcefa:2011_21

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Keywords: Generalized maximum entropy; costs; estimation; Alentejo; FADN.;

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  1. A. Moxey & R. Tiffin, 1994. "Estimating Linear Production Coefficients From Farm Business Survey Data: A Note," Journal of Agricultural Economics, Wiley Blackwell, Wiley Blackwell, vol. 45(3), pages 381-385.
  2. 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, Review of Applied Economics, vol. 1(1).
  3. Yves Léony & Ludo Peeters & Maurice Quinqu & Yves Surry, 1999. "The Use of Maximum Entropy to Estimate Input-Output Coefficients From Regional Farm Accounting Data," Journal of Agricultural Economics, Wiley Blackwell, Wiley Blackwell, vol. 50(3), pages 425-439.
  4. Lence, Sergio H. & Miller, Douglas, 1998. "Recovering Output-Specific Inputs from Aggregate Input Data: A Generalized Cross Entropy Approach," Staff General Research Papers, Iowa State University, Department of Economics 1305, Iowa State University, Department of Economics.
  5. Cesaltina Pires & Andreia Dionisio & Luís Coelho, 2010. "GME versus OLS - Which is the best to estimate utility functions?," CEFAGE-UE Working Papers, University of Evora, CEFAGE-UE (Portugal) 2010_02, University of Evora, CEFAGE-UE (Portugal).
  6. Howitt, Richard E. & Reynaud, Arnaud, 2002. "Spatial Disaggregation of Agricultural Production Data," 2002 International Congress, August 28-31, 2002, Zaragoza, Spain, European Association of Agricultural Economists 24961, European Association of Agricultural Economists.
  7. Zhang, Xiaobo & Fan, Shenggen, 1999. "Estimating crop-specific production technologies in Chinese agriculture: a generalized maximum entropy approach," EPTD discussion papers, International Food Policy Research Institute (IFPRI) 50, International Food Policy Research Institute (IFPRI).
  8. Paris, Quirino & Caputo, Michael R., 2001. "Sensitivity Of The Gme Estimates To Support Bounds," Working Papers, University of California, Davis, Department of Agricultural and Resource Economics 11966, University of California, Davis, Department of Agricultural and Resource Economics.
  9. Iain Fraser, 2000. "An application of maximum entropy estimation: the demand for meat in the United Kingdom," Applied Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 32(1), pages 45-59.
  10. Paul V. Preckel, 2001. "Least Squares and Entropy: A Penalty Function Perspective," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, Agricultural and Applied Economics Association, vol. 83(2), pages 366-377.
  11. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers, Iowa State University, Department of Economics 1488, Iowa State University, Department of Economics.
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