This book aims to fill the gap between panel data econometrics
textbooks, and the latest development on 'big data', especially
large-dimensional panel data econometrics. It introduces important
research questions in large panels, including testing for
cross-sectional dependence, estimation of factor-augmented panel
data models, structural breaks in panels and group patterns in
panels. To tackle these high dimensional issues, some techniques
used in Machine Learning approaches are also illustrated. Moreover,
the Monte Carlo experiments, and empirical examples are also
utilised to show how to implement these new inference methods.
Large-Dimensional Panel Data Econometrics: Testing, Estimation and
Structural Changes also introduces new research questions and
results in recent literature in this field.
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