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Household food expenditures in the United States: A Bayesian MCMC approach to censored equation systems

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  • Kasteridis, Panagiotis P.
  • Yen, Steven T.

Abstract

We apply a Bayesian Markov Chain Monte Carlo (MCMC) technique, along with data augmentation to accommodate censoring in the dependent variables, to the estimation of a large expenditure system of food expenditures. Our finding of significant error covariance estimates justifies estimation of the system in improving statistical efficiency. Income, household composition, regions and other socio-demographic variables are found to play significant roles in determining household food expenditures.

Suggested Citation

  • Kasteridis, Panagiotis P. & Yen, Steven T., 2010. "Household food expenditures in the United States: A Bayesian MCMC approach to censored equation systems," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61763, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea10:61763
    DOI: 10.22004/ag.econ.61763
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    References listed on IDEAS

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    1. Lee, Lung-Fei & Pitt, Mark M, 1986. "Microeconometric Demand Systems with Binding Nonnegativity Constraints: The Dual Approach," Econometrica, Econometric Society, vol. 54(5), pages 1237-1242, September.
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