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Robust Estimators Of Errors-In-Variables Models, Part 1

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  • Paris, Quirino

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.

Suggested Citation

  • Paris, Quirino, 2004. "Robust Estimators Of Errors-In-Variables Models, Part 1," Working Papers 11945, University of California, Davis, Department of Agricultural and Resource Economics.
    • Handle: RePEc:ags:ucdavw:11945
    DOI: 10.22004/ag.econ.11945
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    Research Methods/ Statistical Methods;

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