Abstract: This paper studies a minimum distance moment estimator for general nonlinear regression models with Berkson-type measurement errors in predictor variables. The estimator is based on the first two conditional moments of the response variable given the observed predictor variable. It is shown that under general regularity conditions the proposed estimator is consistent and asymptotically normally distributed.
Key words and phrases: Asymptotic normality, consistency, errors-in-variables, least squares method, minimum distance estimator, moment estimation.