Seemingly Unrelated Regression with Measurement Error: Estimation via Markov chain Monte Carlo and Mean Field Variational Bayes Approximation

TL;DR

This paper introduces Bayesian and variational Bayes methods for estimating a seemingly unrelated regression model with measurement error, addressing identification issues and demonstrating improved model fitting in health data applications.

Contribution

It develops novel Bayesian and variational algorithms for multi-equation regression with measurement error, a topic rarely addressed in econometrics.

Findings

MFVB is computationally efficient for large datasets

Measurement error modeling improves data fit in health studies

Methods perform well across various simulation scenarios

Abstract

Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation methods: a pure Bayesian algorithm (based on Markov chain Monte Carlo techniques) and its mean field variational Bayes (MFVB) approximation. The MFVB method has the added advantage of being computationally fast and can handle big data. An issue pertinent to measurement error models is parameter identification, and this is resolved by employing a prior distribution on the measurement error variance. The methods are shown to perform well in multiple simulation studies, where we analyze the impact on posterior estimates arising due to different values of reliability ratio or variance of the true unobserved quantity used in the data generating process. The paper further implements the proposed algorithms in an application drawn from the health literature and shows that modeling measurement error in the data can improve model fitting.