Spatial autoregressive model with measurement error in covariates

TL;DR

This paper introduces a measurement error-corrected maximum likelihood estimator for the spatial autoregressive model, addressing bias issues caused by covariate measurement errors and demonstrating its effectiveness through simulations and real data applications.

Contribution

It develops a novel ME-QMLE method for SAR models with measurement error, ensuring consistency and asymptotic normality, and applies it to real-world spatial data.

Findings

The estimator corrects bias caused by measurement error.

Simulation results confirm the accuracy of standard error estimates.

Application to real data illustrates practical utility.

Abstract

The Spatial AutoRegressive model (SAR) is commonly used in studies involving spatial and network data to estimate the spatial or network peer influence and the effects of covariates on the response, taking into account the dependence among units. While the model can be efficiently estimated with a Quasi maximum likelihood approach (QMLE), the detrimental effect of covariate measurement error on the QMLE and how to remedy it is currently unknown. If covariates are measured with error, then the QMLE may not have the $\sqrt{n}$ convergence and may even be inconsistent even when a node is influenced by only a limited number of other nodes or spatial units. We develop a measurement error-corrected ML estimator (ME-QMLE) for the parameters of the SAR model when covariates are measured with error. The ME-QMLE possesses statistical consistency and asymptotic normality properties and we derive its limiting covariance. We consider two types of applications. The first is when the true covariate is imprecisely measured with replicated measurements or cannot be measured directly, and a proxy is observed instead. The second one involves including latent homophily factors estimated with error from the network for estimating peer influence. Our numerical results verify the bias correction property of the estimator and the accuracy of the standard error estimates in finite samples. We illustrate the method on two real datasets; i) peer influence in GPA for middle school students in New Jersey and ii) county-level death rates from the COVID-19 pandemic.