Asymptotic Comparison between Maximum Likelihood and Method of Moments in a Particular Errors-in-Variables Regression Model

摘要

The authors study a particular functional errors-in-variables regression model. In the case of no equation error (all randomness due to measurement errors), they show that the maximum likelihood estimator computed assuming normality is asymptotically better than the usual moments estimator, even if the errors are not normally distributed.