Uncovering Hidden Harmony: Latent Binary Quantile Regression and an Application

Abstract

This paper develops a method of latent binary quantile regression for settings in which the binary regressand is unobserved and proxied by multiple indicators. We demonstrate how to identify and estimate parameters for conditional quantiles of the hidden outcome, prove the strong consistency of the estimator, and run Monte Carlo experiments to verify its finite-sample performance. Our empirical application attempts to uncover factors affecting the harmony levels within college dormitory rooms. Among other findings, we discover that sleeping schedule discordance damages relationship. Both our approach and findings are applicable for management research and practice.

Keywords: Latent Binary Quantile Regression, Latent Maximum Score Estimator, Measurement Error, Misclassification, Small Group Harmony, College Dormitory

JEL Classification: C21,C25,I20