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

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.


Issue Date:
2004
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/11945
Total Pages:
60
Series Statement:
Working Paper 04-007




 Record created 2017-04-01, last modified 2017-04-04

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