IDEAS home Printed from
   My bibliography  Save this article

Estimation of cost allocation coefficients at the farm level using an entropy approach


  • Rui Fragoso
  • Maria Leonor da Silva Carvalho


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 Farm Accountancy Data Network 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 the observed data. The entropy approach showed to be a flexible and valid tool to estimate incomplete information, namely regarding farm costs.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1893-1906 DOI: 10.1080/02664763.2013.799127

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    1. Xiaobo Zhang & Shenggen Fan, 2001. "Estimating Crop-Specific Production Technologies in Chinese Agriculture: A Generalized Maximum Entropy Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(2), pages 378-388.
    2. Howitt, Richard E. & Reynaud, Arnaud, 2002. "Spatial Disaggregation of Agricultural Production Data," 2002 International Congress, August 28-31, 2002, Zaragoza, Spain 24961, European Association of Agricultural Economists.
    3. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    4. A. Moxey & R. Tiffin, 1994. "Estimating Linear Production Coefficients From Farm Business Survey Data: A Note," Journal of Agricultural Economics, Wiley Blackwell, vol. 45(3), pages 381-385.
    5. Sergio H. Lence & Douglas J. Miller, 1998. "Recovering Output-Specific Inputs from Aggregate Input Data: A Generalized Cross-Entropy Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(4), pages 852-867.
    6. Cesaltina Pires & Andreia Dionisio & Luís Coelho, 2010. "GME versus OLS - Which is the best to estimate utility functions?," CEFAGE-UE Working Papers 2010_02, University of Evora, CEFAGE-UE (Portugal).
    7. Paris, Quirino & Caputo, Michael R., 2001. "Sensitivity Of The Gme Estimates To Support Bounds," Working Papers 11966, University of California, Davis, Department of Agricultural and Resource Economics.
    8. 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).
    9. Iain Fraser, 2000. "An application of maximum entropy estimation: the demand for meat in the United Kingdom," Applied Economics, Taylor & Francis Journals, vol. 32(1), pages 45-59.
    10. 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, vol. 50(3), pages 425-439.
    11. Paul V. Preckel, 2001. "Least Squares and Entropy: A Penalty Function Perspective," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(2), pages 366-377.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1893-1906. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.