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Sensitivity Of The Gme Estimates To Support Bounds

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  • Paris, Quirino
  • Caputo, Michael R.

Abstract

The claim has been made that the Generalized Maximum Entropy (GME) estimator of Golan, Judge and Miller is not sensitive to variations in the support bounds of either the parameters or the error terms. In this paper, we scrutinized this claim by means of Monte Carlo experiments and found that the parameter estimates are impacted in a substantial way by these changes. We also analyzed the famous data sample on the US manufacturing industry used by Cobb and Douglas in 1934 and found that the GME estimator is very sensitive to changes in support bounds. We conclude with a general result by Caputo and Paris according to which any support bound variation produces unexpected responses in the parameter estimates.

Suggested Citation

  • 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.
  • Handle: RePEc:ags:ucdavw:11966
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    File URL: http://purl.umn.edu/11966
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    1. Douglas J. Miller & Andrew J. Plantinga, 1999. "Modeling Land Use Decisions with Aggregate Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 180-194.
    2. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    3. Caputo, Michael R. & Paris, Quirino, 2008. "Comparative statics of the generalized maximum entropy estimator of the general linear model," European Journal of Operational Research, Elsevier, vol. 185(1), pages 195-203, February.
    4. Barnett, William A. & Jonas, Andrew B., 1983. "The Muntz-Szatz demand system : An application of a globally well behaved series expansion," Economics Letters, Elsevier, vol. 11(4), pages 337-342.
    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.
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    Cited by:

    1. 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.
    2. Louhichi, Kamel & Jacquet, Florence & Butault, Jean Pierre, 2012. "Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 13(2), June.
    3. Fuller, Frank H. & Beghin, John C. & Rozelle, Scott, 2007. "Consumption of dairy products in urban China: results from Beijing, Shangai and Guangzhou," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(4), December.
    4. You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike, 2007. "Generating plausible crop distribution and performance maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach:," IFPRI discussion papers 725, International Food Policy Research Institute (IFPRI).
    5. 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).
    6. 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).
    7. You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike, 2009. "Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach," Agricultural Systems, Elsevier, vol. 99(2-3), pages 126-140, February.

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    Keywords

    Research Methods/ Statistical Methods;

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