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Testing for negativity in a demand system: A Bayesian approach

Author

Listed:
  • Hideo Kozumi

    (Faculty of Economics and Business Administration, Hokkaido University, Nishi 7, Kita 9, Kita-ku, Sapporo 060-0809, Japan)

  • Noriko Hashimoto

    (Faculty of Economics, Kansai University, 3-3-35 Yamata, Suita, Osaka 564-8680, Japan)

  • Hikaru Hasegawa

    (Faculty of Economics and Business Administration, Hokkaido University, Nishi 7, Kita 9, Kita-ku, Sapporo 060-0809, Japan)

Abstract

The paper introduces Bayesian inference into a demand model. This allows us to test for the negativity condition of the substitution matrix which is difficult to handle directly in the traditional approach. To illustrate the Bayesian inference procedures, we estimate the Rotterdam model and test the demand properties using Japanese data. The empirical results show the importance of specifically considering negativity in demand analysis.

Suggested Citation

  • Hideo Kozumi & Noriko Hashimoto & Hikaru Hasegawa, 1999. "Testing for negativity in a demand system: A Bayesian approach," Empirical Economics, Springer, vol. 24(2), pages 211-223.
  • Handle: RePEc:spr:empeco:v:24:y:1999:i:2:p:211-223
    Note: received: September 1997/final version received: February 1998
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    Citations

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    Cited by:

    1. Eales, James S. & Henderson, Jason R., 2000. "The Impact Of Negativity On Separability Testing," 2000 Annual meeting, July 30-August 2, Tampa, FL 21817, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Cranfield, J. A. L. & Pellow, Scott, 2004. "The role of global vs. local negativity in functional form selection: an application to Canadian consumer demands," Economic Modelling, Elsevier, vol. 21(2), pages 345-360, March.
    3. J. M. Gil & B. Dhehibi & M. Ben Kaabia & A. M. Angulo, 2004. "Non-stationarity and the import demand for virgin olive oil in the European Union," Applied Economics, Taylor & Francis Journals, vol. 36(16), pages 1859-1869.

    More about this item

    Keywords

    Markov chain Monte Carlo (MCMC) · conditional predictive ordinate (CPO) · Bayes factor · Rotterdam model;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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