Binary choice with misclassification and social interactions, with an application to peer effects in attitude

The interaction of economic agents is one of the most important elements in economic analyses. Social interactions on subjective outcomes, behavior, or decisions, are inherently difficult to identify and estimate because these variables are prone to misclassification errors. This paper puts forth a binary choice model with misclassification and social interactions to rectify the misclassification problems in social interactions studies. We achieve the identification of the conditional choice probability of the latent dependent variable by the technique of repeated measurements and a monotonicity condition. We construct the complete likelihood function from the two repeated measurements and propose a nested pseudo likelihood algorithm for estimation. Consistency and asymptotic normality results are shown for the proposed estimation method. We illustrate the finite sample performance of the model and the estimation method by three Monte Carlo experiments and an application to the study of peer effects among students in their attitudes towards learning.

Automated summary

This paper proposes a binary choice model with misclassification and social interactions to address the misclassification problems in social interactions studies. The identification of the conditional choice probability of the latent dependent variable is achieved using repeated measurements and a monotonicity condition. The complete likelihood function is constructed from the two repeated measurements, and a nested pseudo likelihood algorithm is proposed for estimation. Consistency and asymptotic normality results are shown for the proposed estimation method.