Large-Dimensional Panel Data Econometrics

The following sections are included:

• Preface
• About the Authors
• Contents

This book is motivated by the recent development in high-dimensional panel data models with large amount of individuals/countries (n) and observations over time (T). Specifically, it introduces four important research topics in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural changes and group patterns in panels in the following four chapters. To address these issues, we examine the properties of traditional tests and estimators in large-dimensional setup. In addition, we also take advantage of some techniques in Random Matrix Theory and Machine Learning…

In recent literature, cross-sectional dependence among individuals is a concern when n is large. As serial correlation in time-series analysis, the cross-sectional of dependence/correlation could invalidate inference. In some cases, it could even render inconsistent estimation. This chapter discusses testing procedures in the fixed effects panel data models, including static and dynamic cases…

The following sections are included:

• Motivation
• CCE Approach
• IPC Approach
• Likelihood Approach
• Other Studies
• An Empirical Example
• Exercises

Based on Baltagi, Feng and Kao (2016, 2019), this chapter studies the issue of structural changes in large panel data regression models. Parameter instability due to new policy implementation or major technological shocks is more likely to occur over a longer time span. Consequently, ignoring structural changes may lead to inconsistent estimation and invalid inference…

The following sections are included:

• Panel Latent Group Structure Models
• K-means Clustering
• Conclusion
• Exercises

The following sections are included:

• Bibliography
• Index