Asymptotic normality of a simple linear EV regression model with martingale difference errors

Guo-Liang Fan, Tian-Heng Chen


This paper considers the estimation of a
linear EV (errors-in-variables) regression model under martingale
difference errors. The usual least squares estimations lead to
biased estimators of the unknown parametric when measurement errors
are ignored. By correcting the attenuation we propose a modified
least squares estimator for a parametric component and construct the
estimators of another parameter component and error variance. The
asymptotic normalities are also obtained for these estimators. The
simulation study shows the modified least squares estimations
perform better than the least squares estimations.

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