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• W2913871128 abstract "Several contemporary models of consumer demand comprise complete sets of nonlinear demand functions. Estimation methods should take into account parameter nonlinearity, crossequation 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. A major topic in the literature of theoretical and applied econometrics is the estimation of systems of nonlinear equations. In particular, several basic contemporary models of consumer demand comprise sets of nonlinear demand functions.l Estimation methods should take into account essential parameter nonlinearity, cross-equation correlation, variance-covariance singularity of the disturbance terms, and various parameter restrictions. Consequently, practitioners are continually faced with theoretically satisfactory models imbued with practical difficulties. Two viable methods for estimating nonlinear demand systems are the maximum likelihood (ML) procedure and the iterative version of Zellner's seemingly unrelated regression (IZEF) procedure." @default.
• W2913871128 created "2019-02-21" @default.
• W2913871128 creator A5009607258 @default.
• W2913871128 date "1983-07-01" @default.
• W2913871128 modified "2023-09-22" @default.
• W2913871128 title "Alternative Estimation Methods of Nonlinear Demand Systems" @default.
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