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Inferring The Nutrient Content Of Food With Prior Information

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  • LaFrance, Jeffrey T.

Abstract

Given measurements on the nutrient content of the U.S. food supply and a coherent reduced form empirical model of the demand for foods, we can analyze the effect of agricultural farm and food policy on nutrition. Using unpublished documents from the HNIS, estimates of the percentages of seventeen nutrients supplied by twenty-one foods were compiled for the period 1952-1983. The Bayesian Method of Moments is applied to this data set to obtain a proper prior for the purpose of drawing year-to-year inferences about the nutrient content of the U.S. food supply for the period 1909-1994. Information theory and the Kullback-Leibler cross entropy criterion are used to formalize the inference problem.

Suggested Citation

  • LaFrance, Jeffrey T., 1998. "Inferring The Nutrient Content Of Food With Prior Information," CUDARE Working Papers 25067, University of California, Berkeley, Department of Agricultural and Resource Economics.
    • Handle: RePEc:ags:ucbecw:25067
    DOI: 10.22004/ag.econ.25067
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    1. LaFrance, Jeffrey T., 2008. "The structure of US food demand," Journal of Econometrics, Elsevier, vol. 147(2), pages 336-349, December.
    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. Jeffrey T. LaFrance & W. Michael Hanemann, 1989. "The Dual Structure of Incomplete Demand Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 262-274.
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    Cited by:

    1. Scott E. Atkinson & Jeffrey H. Dorfman, 2009. "Feasible estimation of firm-specific allocative inefficiency through Bayesian numerical methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 675-697.
    2. Wu, Ximing & Perloff, Jeffrey M. & Golan, Amos, 2002. "Effects of Government Policies on Income Distribution and Welfare," CUDARE Working Papers 25031, University of California, Berkeley, Department of Agricultural and Resource Economics.
    3. Arnold Zellner, 2003. "Some Recent Developments in Econometric Inference," Econometric Reviews, Taylor & Francis Journals, vol. 22(2), pages 203-215.
    4. Atkinson, Scott E. & Dorfman, Jeffrey H., 2005. "Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution," Journal of Econometrics, Elsevier, vol. 126(2), pages 445-468, June.
    5. Shen, Edward Z. & Perloff, Jeffrey M., 2001. "Maximum entropy and Bayesian approaches to the ratio problem," Journal of Econometrics, Elsevier, vol. 104(2), pages 289-313, September.
    6. Zellner, Arnold, 2006. "S. James Press And Bayesian Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 10(5), pages 667-684, November.
    7. Golan, Amos, 2001. "A simultaneous estimation and variable selection rule," Journal of Econometrics, Elsevier, vol. 101(1), pages 165-193, March.
    8. Arnold Zellner, 2001. "Remarks on a 'critique' of the Bayesian Method of Moments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 775-778.
    9. Arnold Zellner, 2009. "Honorary Lecture on S. James Press and Bayesian Analysis," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 1(1), pages 98-118, September.
    10. Timothy K.M. Beatty, 2007. "Recovering the Shadow Value of Nutrients," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(1), pages 52-62.
    11. Zellner, Arnold, 2002. "Information processing and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 41-50, March.
    12. Zellner, Arnold, 2007. "Some aspects of the history of Bayesian information processing," Journal of Econometrics, Elsevier, vol. 138(2), pages 388-404, June.
    13. Wright, Melissa A. & Beatty, Timothy K.M. & Chouinard, Hayley H., 2020. "Do firms leverage the FDA nutrient label rounding rules to generate favorable nutrition fact panels or health claims?," Food Policy, Elsevier, vol. 91(C).

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