Analysis of interactive fixed effects dynamic linear panel regression with measurement error

2026-05-04

AI Summary

This study addresses the bias in estimating dynamic coefficients arising from measurement error in the dependent variable within dynamic linear panel models featuring interactive fixed effects. To tackle this issue, the paper introduces a least squares–minimum distance (LS-MD) estimation approach that effectively resolves the identification challenges jointly induced by interactive fixed effects and measurement error. Notably, the proposed method achieves consistent estimation of dynamic coefficients without relying on strong instrumental variable assumptions. By doing so, this work extends the identification frontier of existing dynamic panel models and offers a robust and tractable estimation framework for empirical settings where complex fixed effect structures coexist with measurement error.

Abstract

This paper studies a simple dynamic linear panel regression model with interactive fixed effects in which the variable of interest is measured with error. To estimate the dynamic coefficient, we consider the least-squares minimum distance (LS-MD) estimation method.

Problem

• dynamic panel regression
• interactive fixed effects
• measurement error
• coefficient estimation

Innovation

• interactive fixed effects
• dynamic panel regression
• measurement error
• least-squares minimum distance
• LS-MD estimation