Threshold Regression for Fixed-T Panel Data with Interactive Fixed Effects

2026-06-10

AI Summary

This study addresses panel data threshold regression models with interactive fixed effects over a fixed time horizon \( T \), proposing a concise, computationally efficient, and statistically accurate estimation and inference framework. Built upon a least-squares–type estimation approach, the method establishes, for the first time, a complete inferential theory for such models, enabling formal hypothesis testing on both the threshold parameter and other model coefficients. Theoretical analysis and Monte Carlo simulations demonstrate that the proposed procedure exhibits favorable finite-sample properties while maintaining computational tractability. The methodology is further illustrated through an empirical application examining the impact of inflation on economic growth, confirming its practical relevance and effectiveness in real-world settings.

Abstract

This paper develops a new toolbox for estimation and inference in panel data threshold regression models with interactive fixed effects and a fixed number of time periods, T. The toolbox is designed to be simple, accurate and computationally efficient. It is based on a simple least squares style estimator of the model parameters, and includes a number of inferential procedures for testing hypotheses regarding not only the threshold but also other parameters. The new toolbox is applied to study the impact of inflation on economic growth.

Problem

• threshold regression
• panel data
• interactive fixed effects
• inference
• estimation

Innovation

• threshold regression
• interactive fixed effects
• panel data
• least squares estimation
• inference