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Recovering Output-Specific Inputs from Aggregate Input Data: A Generalized Cross Entropy Approach

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  • Lence, Sergio H.
  • Miller, Douglas

Abstract

For multiproduct firms, data on aggregate input usage are typically available but data on activity-specific inputs are not. The present study proposes a generalized cross-entropy approach to estimate activity-specific input allocations that are consistent with the aggregate information. The proposed method does not require behavioral assumptions (e.g., profit maximization) but does accommodate behavioral restrictions as well as nonsample information about the plausible factor shares across enterprises. Monte Carlo experiments using simulated data for multifactor-multiproduct firms are used to evaluate the performance of the proposed method.

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Bibliographic Info

Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 1305.

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Date of creation: 01 Nov 1998
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Publication status: Published in American Journal of Agricultural Economics, November 1998, vol. 80 no. 4, pp. 852-867
Handle: RePEc:isu:genres:1305

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Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
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Web page: http://www.econ.iastate.edu
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Cited by:
  1. Rui Fragoso & Maria Leonor Carvalho, 2011. "Estimation of Cost Allocation Coefficients at the Farm Level Using an Entropy Approach," CEFAGE-UE Working Papers 2011_21, University of Evora, CEFAGE-UE (Portugal).
  2. Msangi, Siwa & Howitt, Richard E., 2006. "Estimating Disaggregate Production Functions: An Application to Northern Mexico," 2006 Annual meeting, July 23-26, Long Beach, CA 21080, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  3. Howitt, Richard E. & Msangi, Siwa, 2002. "Reconstructing Disaggregate Production Functions," 2002 Annual meeting, July 28-31, Long Beach, CA 19585, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  4. Hansen, Heiko & Surry, Yves R., 2006. "Die Schatzung Verfahrensspezifischer Faktoreinsatzmengen Fur Die Landwirtschaft In Deutschland," 46th Annual Conference, Giessen, Germany, October 4-6, 2006 14959, German Association of Agricultural Economists (GEWISOLA).
  5. Tack, Jesse, 2013. "A Nested Test for Common Yield Distributions with Applications to U.S. Corn," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(1), April.
  6. 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.
  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. Akpalu, Wisdom & Hassan, Rashid M. & Ringler, Claudia, 2008. "Climate variability and maize yield in South Africa: Results from GME and MELE methods," IFPRI discussion papers 843, International Food Policy Research Institute (IFPRI).
  9. Wikstrom, Daniel & Peeters, Ludo & Surry, Yves R., 2011. "Semiparametric Cost Allocation Estimation," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115742, European Association of Agricultural Economists.

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