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Generalised Maximum Entropy Estimation and Heterogeneous Technologies

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  • Lansink, Alfons Oude

Abstract

Generalised maximum entropy methods are used to estimate a dual model of production on panel data of Dutch cash crop farms over the period 1970-1992. The generalised maximum entropy approach allows a coherent system of input demand and output supply equations to be estimated for each farm in the sample, thus capturing technological heterogeneity. The estimation results are used to perform a cluster analysis to identify groups of farms with similar technologies. Copyright 1999 by Oxford University Press.

Suggested Citation

  • Lansink, Alfons Oude, 1999. "Generalised Maximum Entropy Estimation and Heterogeneous Technologies," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 26(1), pages 101-115, March.
    • Handle: RePEc:oup:erevae:v:26:y:1999:i:1:p:101-15
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    Citations

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    Cited by:

    1. Heckelei, Thomas & Mittelhammer, Ronald C. & Jansson, Torbjorn, 2008. "A Bayesian Alternative To Generalized Cross Entropy Solutions For Underdetermined Econometric Models," Discussion Papers 56973, University of Bonn, Institute for Food and Resource Economics.
    2. Heckelei, Thomas & Britz, Wolfgang, 2000. "Positive Mathematical Programming with Multiple Data Points: A Cross-Sectional Estimation Procedure," Cahiers d'Economie et de Sociologie Rurales (CESR), Institut National de la Recherche Agronomique (INRA), vol. 57.
    3. Britz, Wolfgang & van Ittersum, Martin K. & Oude Lansink, Alfons G.J.M. & Heckelei, Thomas, 2012. "Tools for Integrated Assessment in Agriculture. State of the Art and Challenges," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(2), pages 1-26, August.
    4. Heckelei, Thomas & Wolff, Hendrik, 2002. "A Methodological Note on the Estimation of Programming Models," 2002 International Congress, August 28-31, 2002, Zaragoza, Spain 24896, European Association of Agricultural Economists.
    5. Zheng, Y. & Gohin, A., 2018. "Estimating dynamic stochastic decision models: explore the generalized maximum entropy alternative," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276001, International Association of Agricultural Economists.
    6. Philippe Koutchade & Alain Carpentier & Fabienne Féménia, 2015. "Empirical modeling of production decisions of heterogeneous farmers with random parameter models," Working Papers SMART 15-10, INRAE UMR SMART.
    7. Heckelei, T. & Wolff, H., 2001. "Ansätze zur (Auf-)Lösung eines alten Methodenstreits: Ökonometrische Spezifikation von Programmierungsmodellen zur Agrarangebotsanalyse," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 37.
    8. Lips, Markus, 2012. "Joint Cost Allocation by Means of Maximum Entropy," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126675, International Association of Agricultural Economists.
    9. Hansen, H. & Surry, Y., 2007. "Die Schätzung verfahrensspezifischer Faktoreneinsatzmengen für die Landwirtschaft in Deutschland," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 42, March.

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