Estimation and exclusion restrictions in clustered linear models

2025/08/18 by Anna Mikusheva, Mikusheva, Anna, Mikkel Sølvsten +2 · 1 citation
Computer Science · #Bayesian Methods and Mixture Models #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2508.12860

openalex publication_date 2025/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We study linear regression models with clustered data, high-dimensional controls, and intricate exclusion restrictions. We propose a correctly centered internal instrument IV estimator that accommodates a broad class of exclusion restrictions and allows within-cluster dependence. The estimator admits a simple leave-out interpretation and is computationally tractable. We derive a central limit theorem for the associated quadratic form and propose a robust variance estimator. We also develop identification-robust inference procedures. Our framework extends dynamic panel methods to general clustered settings. We illustrate the approach in a large-scale fiscal intervention in rural Kenya, where spatial interference generates the exclusion-restriction pattern.

Citations

• Can a Trusted Messenger Change Behavior When Information Is Plentiful? Evidence from the First Months of the COVID-19 Pandemic in West Bengal
• Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference
• What Estimators Are Unbiased For Linear Models?
• Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method
• Asymptotic Theory for Clustered Samples
• Inference in Linear Regression Models with Many Covariates and Heteroskedasticity
• Another look at the instrumental variable estimation of error-components models
• Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations

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