Comparison of three estimators in a polynomial regression with measurement errors

In a polynomial regression with measurement errors in the
covariate, which is supposed to be normally distributed, one has
(at least) three ways to estimate the unknown regression
parameters: one can apply ordinary least squares (OLS) to the model
without regard of the measurement error or one can correct for the
measurement error, either by correcting the estimating equation
(ALS) or by correcting the mean and variance functions of the
dependent variable, which is done by conditioning on the
observable, error ridden, counter part of the covariate (SLS).
While OLS is biased the other two estimators are consistent. Their
asymptotic covariance matrices can be compared to each other, in
particular for the case of a small measurement error variance.