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Stochastic Specification and Maximum-Likelihood Estimation of the Linear Expenditure System

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In this paper, the problem of stochastic specification and maximum-likelihood estimation of the linear expenditure system (LES) is addressed, making full use of the restrictions of economic theory by assuming that the minimum required quantities for the commodities have a threeparameter multivariate lognormal distribution.
Abstract
From the point of view of consumer demand theory the linear expenditure system (LES) provides a convenient model for representing consumer response to price and income and its linearity is one of its most attractive features. But when estimation problems are discussed, the descriptive adjective is more notable for its irony than its accuracy. Since Stone (1954) first calculated parameter estimates for the LES, some stochastic specifications for the system have been given. These specifications, however, ignore some of the requirements implied by economic theory and these methods of estimation lack desirable properties. This paper will deal with the problems of stochastic specification and maximum-likelihood estimation of the LES making full use of the restrictions of economic theory by assuming that the minimum required quantities for the commodities have a three-parameter multivariate lognormal distribution.

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Munich Personal RePEc Archive
Stochastic Specification and
Maximum-Likelihood Estimation of the
Linear Expenditure System
Chipman, John S. and Tian, Guoqiang
1989
Online at https://mpra.ub.uni-muenchen.de/41385/
MPRA Paper No. 41385, posted 17 Sep 2012 13:35 UTC





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References
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Book

Linear statistical inference and its applications

TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.

The behavior of maximum likelihood estimates under nonstandard conditions

TL;DR: In this paper, the authors prove consistency and asymptotic normality of maximum likelihood estimators under weaker conditions than usual, such that the true distribution underlying the observations belongs to the parametric family defining the estimator, and the regularity conditions do not involve the second and higher derivatives of the likelihood function.
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Linear Statistical Inference and its Applications

TL;DR: The theory of least squares and analysis of variance has been studied in the literature for a long time, see as mentioned in this paper for a review of some of the most relevant works. But the main focus of this paper is on the analysis of variance.
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An Almost Ideal Demand System

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