Higher moment estimators for linear regression models with errors in the variables

This paper proposes consistent instrumental variable estimators for linear regression models with errors in the variables that require no extraneous information. These estimators are based on sample moments of order higher than two. While similar estimators proposed previously in the literature seem to be quite erratic, our experimental f findings suggest that our estimators perform better than ordinary least squares estimators in terms of root mean squared errors and also in terms of size of type I errors of standard tests in many typical situations of economic analyses. Tests for the presence of errors in the variables are also described.