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.Contents: PrefaceAbout the AuthorsIntroductionTests for Cross-Sectional Dependence in Fixed Effects Panel Data ModelsFactor Augmented Panel Data Regression ModelsStructural Changes in Panel Data ModelsLatent-Grouped Structure in Panel Data ModelsBibliographyIndex
Readership: Targeted readers include advanced undergraduates and PhD students and researchers in economics, statistics and business subjects. This book can be used as a textbook or reference book in an advanced undergraduate or graduate level econometrics course. Correlated Effects;Factor Model;Iterated Principal Components;Structural Change;Common Break;Grouped Pattern;K-means;LASSO;Endogeneity0Key Features:It provides a guidebook to PhD students and junior faculty who are interested in how traditional inference methods using economic data are affected by large dimension setups, for e.g., large panels and abundance of dataDifferent from other textbooks, this book also discusses details of research techniques related to high dimensional issues in econometrics, in addition to introducing new research questions and results in recent literature