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Estimation of Models with Variable Coefficients

John E. Jackson
Political Analysis
Vol. 3 (1991), pp. 27-49
Stable URL: http://www.jstor.org/stable/23317756
Page Count: 23
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Estimation of Models with Variable Coefficients
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Abstract

The ordinary least squares (OLS) estimator gives biased coefficient estimates if coefficients are not constant for all cases but vary systematically with the explanatory variables. This article discusses several different ways to estimate models with systematically and randomly varying coefficients using estimated generalized least squares and maximum likelihood procedures. A Monte Carlo simulation of the different methods is presented to illustrate their use and to contrast their results to the biased results obtained with ordinary least squares. Several applications of the methods are discussed and one is presented in detail. The conclusion is that, in situations with variables coefficients, these methods offer relatively easy means for overcoming the problems.

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