This paper studies the identification of the coefficients in a linear equation when data on the outcome, covariates, and an error-laden proxy for a latent variable are available. We maintain that the measurement error in the proxy is classical and relax the assumption that the proxy is excluded...

We characterize the sharp identification regions for the coefficients in a system of linear equations that share an explanatory variable measured with classical error. We demonstrate the identification gain from analyzing the equations jointly. We derive the sharp identification regions under...