Two-Way Mean Group Estimators for Heterogeneous Panel Models with Fixed T

2025-08-13

AI Summary

This paper addresses the challenge of consistently estimating and conducting valid inference on the expected slope coefficients in heterogeneous random-coefficient panel models with fixed time dimension (T), under the joint presence of two-way fixed effects and interactive fixed effects. We propose the Two-Way Mean Group (TW-MG) estimator and integrate it with a jackknife bias correction (leave-one-out) and a Hausman-type pooling test, thereby establishing a unified inference framework tailored for the small-(T), large-(N) setting. The estimator consistently identifies the average slope coefficient while robustly accommodating unobserved common factors and both individual- and time-specific heterogeneity. Monte Carlo simulations demonstrate its superior finite-sample performance relative to alternatives. Empirical applications to the health expenditure–income relationship and production function estimation yield robust and economically plausible results, confirming the method’s practical reliability.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Multi-instance/Multi-view Learning
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We consider a correlated random coefficient panel data model with two-way fixed effects and interactive fixed effects in a fixed T framework. We propose a two-way mean group (TW-MG) estimator for the expected value of the slope coefficient and propose a leave-one-out jackknife method for valid inference. We also consider a pooled estimator and provide a Hausman-type test for poolability. Simulations demonstrate the excellent performance of our estimators and inference methods in finite samples. We apply our new methods to two datasets to examine the relationship between health-care expenditure and income, and estimate a production function.

Problem

• Estimating slope coefficients in panel data with fixed effects
• Developing valid inference methods for heterogeneous panel models
• Testing poolability and applying methods to real-world datasets

Innovation

• Two-way mean group estimator for heterogeneous panels
• Jackknife method for valid inference
• Hausman-type test for poolability