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Alternative Estimation Methods Of Nonlinear Demand Systems

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  • Capps, Oral, Jr.

Abstract

Several contemporary models of consumer demand comprise complete sets on nonlinear demand functions. Estimation methods should take into account parameter nonlinearity, cross-equation correlation, variance-covariance singularity of the disturbance terms, and various parameter restrictions. This paper presents a theoretical discussion and some empirical results using the maximum likelihood (ML) method and the iterative version of Zellner's seemingly unrelated regression (IZEF) method in the estimation of a nonlinear system of demand equations (the linear expenditure system) when the disturbance terms are both contemporaneously and serially correlated. On the basis of the evaluation of parameter estimates and their asymptotic standard errors as well as the cost of computation effort, the IZEF technique is preferred over the ML technique in this empirical problem.

Suggested Citation

  • Capps, Oral, Jr., 1983. "Alternative Estimation Methods Of Nonlinear Demand Systems," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 8(1), pages 1-14, July.
    • Handle: RePEc:ags:wjagec:32484
    DOI: 10.22004/ag.econ.32484
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    References listed on IDEAS

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    1. Beach, Charles M & MacKinnon, James G, 1979. "Maximum Likelihood Estimation of Singular Equation Systems with Autoregressive Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 20(2), pages 459-464, June.
    2. BARTEN, Anton P., 1969. "Maximum likelihood estimation of a complete system of demand equations," LIDAM Reprints CORE 34, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Barten, A. P., 1969. "Maximum likelihood estimation of a complete system of demand equations," European Economic Review, Elsevier, vol. 1(1), pages 7-73.
    4. Pollak, Robert A & Wales, Terrence J, 1969. "Estimation of the Linear Expenditure System," Econometrica, Econometric Society, vol. 37(4), pages 611-628, October.
    5. Braithwait, Steven D, 1980. "The Substitution Bias of the Laspeyres Price Index: An Analysis Using Estimated Cost-of-Living Indexes," American Economic Review, American Economic Association, vol. 70(1), pages 64-77, March.
    6. Gallant, A. Ronald, 1975. "Seemingly unrelated nonlinear regressions," Journal of Econometrics, Elsevier, vol. 3(1), pages 35-50, February.
    7. Berndt, Ernst R & Savin, N Eugene, 1975. "Estimation and Hypothesis Testing in Singular Equation Systems with Autoregressive Disturbances," Econometrica, Econometric Society, vol. 43(5-6), pages 937-957, Sept.-Nov.
    8. Green, Richard D. & Hassan, Zuhair A. & Johnson, Stanley R., 1978. "Maximum likelihood estimation of linear expenditure systems with serially correlated errors : An application," European Economic Review, Elsevier, vol. 11(2), pages 207-219, August.
    9. Constantino Lluch & R. Williams, 1975. "Consumer Demand Systems and Aggregate Consumption in the US: An Application of the Extended Linear Expenditure System," Canadian Journal of Economics, Canadian Economics Association, vol. 8(1), pages 49-66, February.
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    Cited by:

    1. Menkhaus, Dale J. & St. Clair, James S. & Hallingbye, Stig, 1985. "A Reexamination Of Consumer Buying Behavior For Beef, Pork, And Chicken," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 10(1), pages 1-10, July.
    2. Tonsor, Glynn T. & Kastens, Terry L., 2006. "How Much Do Starting Values Really Matter? An Empirical Comparison of Genetic Algorithm and Traditional Approaches," 2006 Annual meeting, July 23-26, Long Beach, CA 21252, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Gharibnavaz, M. Reza & Waschik, Robert, 2012. "A General Equilibrium Analysis of Alternative Scenarios for Food and Energy Subsidy Reforms in Iran," Conference papers 332241, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

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