Nonclassical Measurement Error in the Outcome Variable

09/26/2020 ∙ by Christoph Breunig, et al. ∙ 0

We study a semi-/nonparametric regression model with a general form of nonclassical measurement error in the outcome variable. We show equivalence of this model to a generalized regression model. Our main identifying assumptions are a special regressor type restriction and monotonicity in the nonlinear relationship between the observed and unobserved true outcome. Nonparametric identification is then obtained under a normalization of the unknown link function, which is a natural extension of the classical measurement error case. We propose a novel sieve rank estimator for the regression function. We establish a rate of convergence of the estimator which depends on the strength of identification. In Monte Carlo simulations, we find that our estimator corrects for biases induced by measurement errors and provides numerically stable results. We apply our method to analyze belief formation of stock market expectations with survey data from the German Socio-Economic Panel (SOEP) and find evidence for non-classical measurement error in subjective belief data.



There are no comments yet.


page 6

page 20

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.