ROBUST ESTIMATORS OF ERRORS-IN-VARIABLES MODELS, PART 1

Quirino Paris

No 11945, Working Papers from University of California, Davis, Department of Agricultural and Resource Economics

Abstract: It is well known that consistent estimators of errors-in-variables models require knowledge of the ratio of error variances. What is not well known is that a Joint Least Squares estimator is robust to a wide misspecification of that ratio. Through a series of Monte Carlo experiments we show that an easy-to-implement estimator produces estimates that are nearly unbiased for a wide range of the ratio of error variances. These MC analyses encompass linear and nonlinear specifications and also a system on nonlinear equations where all the variables are measured with errors.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 60
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:ags:ucdavw:11945

DOI: 10.22004/ag.econ.11945

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