Shrinkage Estimation and Identification of Latent Group Structures in Panel Data with Interactive Fixed Effects
Bibliographic Data
| ID | 19417350 |
|---|---|
| Authors | Ali Mehrabani (0000-0002-1848-5582, University of Kansas), Shahnaz Parsaeian (University of Kansas, corresponding author) |
| Year | 2026 |
| Pages | 1-13 |
| Publication date | 2026-01-23 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2025.2571185 |
| OpenAlex | W4415036244 |
| Language | EN |
| Citations received | 1 |
| References cited | 46 |
This paper provides a framework for joint shrinkage estimation and identification of latent group structures in panel data models with interactive fixed effects and large number of explanatory variables. The latent structure of the model allows individuals to be classified into a number of groups where the number of groups and/or each individual’s group identity are unknown. A doubly penalized principal component estimation procedure using a pairwise fusion penalty and an adaptive LASSO (least absolute shrinkage and selection operator) penalty is introduced to detect the latent group structure and select the relevant regressors. To implement the proposed approach, an alternating direction method of multipliers algorithm has been developed. The proposed method is further illustrated by simulation studies and an empirical application of economic growth across various countries which demonstrate the good performance of the method
Estimation · Panel data · Shrinkage · Housing Market and Economics · Spatial and Panel Data Analysis · Statistical Methods and Inference
Chapter 8 Growth Econometrics
Model Selection and Estimation in Regression with Grouped Variables
Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors
The Adaptive Lasso and Its Oracle Properties
Growth Empirics
Determining the Number of Factors in Approximate Factor Models
Grouped Patterns of Heterogeneity in Panel Data
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
The Next Generation of the Penn World Table
Economic Growth in a Cross Section of Countries
Sieve Estimation of Time-Varying Panel Data Models With Latent Structures
Homogeneity and Sparsity Analysis for High-Dimensional Panel Data Models
Shrinkage Estimation and Identification of Latent Group Structures in Panel Data with Interactive Fixed Effects
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |