Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data
This addresses the need for efficient group structure estimation in panel data analysis, offering a computationally feasible method that can handle large datasets and limited data availability, though it is incremental in nature.
The paper tackles the problem of estimating unknown group structures in panel data by leveraging variance information from individual coefficient estimates, and demonstrates superior performance through simulations and empirical applications.
Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar effects of observed characteristics, but the grouping is typically unknown in advance. We first conduct a local analysis which reveals that the variances of the individual coefficient estimates contain useful information for the estimation of group structure. We then propose a method to estimate unobserved groupings for general panel data models that explicitly account for the variance information. Our proposed method remains computationally feasible with a large number of individuals and/or repeated measurements on each individual. The developed ideas can also be applied even when individual-level data are not available and only parameter estimates together with some quantification of estimation uncertainty are given to the researcher. A thorough simulation study demonstrates superior performance of our method than existing methods and we apply the method to two empirical applications.