On consistency of the least squares estimators in linear errors-in-variables models with infinite variance errors

This paper deals simultaneously wit h and functional errors-in-variables models (SEIVM and FEIVM), revisiting in this context the ordinary least squares estimators (LSE) for the slope and intercept of the corresponding simple linear regression. It has been known that subject to some model conditions, these estimators become weakly and strongly consistent in the linear SEIVM and FEIVM with the measurement errors having finite variances when the explanatory variables have an infinite variance in the SEIVM, and a similar infinite spread in the FEIVM, while otherwise, the LSE's require an adjustment for consistency with the so-called reliability ratio. In this paper, weak and strong consistency, with and without the possible rates of convergence being determined, is proved for the LSE's of the slope and intecept, assuming that the measurement errors are in the domain of attraction of the normal law (DAN) and thus are, for the first time, allowed to have infinite variances. Moreover, these results are obtained under the conditions that the explanatory variables are in DAN, have an infinite variance, and dominate the measurement errors in terms of variation in the SEIVM, and under appropriately matching versions of these conditions in the FEIVM. This duality extends a previously known interplay between SEIVN 's and FEIVM's.