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A Bayesian Alternative To Generalized Cross Entropy Solutions For Underdetermined Econometric Models

  • Heckelei, Thomas
  • Mittelhammer, Ronald C.
  • Jansson, Torbjorn

This paper presents a Bayesian alternative to Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) methods for deriving solutions to econometric models represented by underdetermined systems of equations. For certain types of econometric model specifications, the Bayesian approach provides fully equivalent results to GME-GCE techniques. However, in its general form, the proposed Bayesian methodology allows a more direct and straightforwardly interpretable formulation of available prior information and can reduce significantly the computational effort involved in finding solutions. The technique can be adapted to provide solutions in situations characterized by either informative or uninformative prior information.

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File URL: http://purl.umn.edu/56973
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Paper provided by University of Bonn, Institute for Food and Resource Economics in its series Discussion Papers with number 56973.

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Date of creation: 2008
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Handle: RePEc:ags:ubfred:56973
Contact details of provider: Web page: http://www.ilr1.uni-bonn.de/

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  1. Robinson, Sherman & Cattaneo, Andrea & El-Said, Moataz, 2000. "Updating and estimating a Social Accounting Matrix using cross entropy methods," TMD discussion papers 58, International Food Policy Research Institute (IFPRI).
  2. Quirino Paris & Richard E. Howitt, 1998. "An Analysis of Ill-Posed Production Problems Using Maximum Entropy," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 124-138.
  3. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
  4. Zhang, Xiaobo & Fan, Shenggen, 1999. "Estimating crop-specific production technologies in Chinese agriculture: a generalized maximum entropy approach," EPTD discussion papers 50, International Food Policy Research Institute (IFPRI).
  5. Robilliard, Anne-Sophie & Robinson, Sherman, 1999. "Reconciling household surveys and national accounts data using a cross entropy estimation method:," TMD discussion papers 50, International Food Policy Research Institute (IFPRI).
  6. Thomas Heckelei & Hendrik Wolff, 2003. "Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 30(1), pages 27-50, March.
  7. Golan, Amos & Judge, George & Robinson, Sherman, 1994. "Recovering Information from Incomplete or Partial Multisectoral Economic Data," The Review of Economics and Statistics, MIT Press, vol. 76(3), pages 541-49, August.
  8. 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.
  9. Lansink, Alfons Oude, 1999. "Generalised Maximum Entropy Estimation and Heterogeneous Technologies," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 26(1), pages 101-15, March.
  10. Arndt, Channing & Robinson, Sherman & Tarp, Finn, 2002. "Parameter estimation for a computable general equilibrium model: a maximum entropy approach," Economic Modelling, Elsevier, vol. 19(3), pages 375-398, May.
  11. Quirino Paris, 2001. "Symmetric Positive Equilibrium Problem: A Framework for Rationalizing Economic Behavior with Limited Information," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(4), pages 1049-1061.
  12. Richard Howitt & Arnaud Reynaud, 2003. "Spatial disaggregation of agricultural production data using maximum entropy," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 30(3), pages 359-387, September.
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