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With the availability of Big Data, one may have more information to identify the underlying causality of economic relationship or forecast important macroeconomic variables or indicators. However, when large volume of data is involved, large dimension could be an issue in the statistical inference of traditional regression models. This book is motivated by the recent development in panel data models with large individuals/countries (n) and large amount of observations over time (T). It introduces testing for cross-sectional dependence and structural breaks in large panels. This book also summarizes important advancement in estimating factor-augmented panel data models and group patterns in panels in recent literature.

This book can be considered complementary to popular panel data econometrics textbooks such as Baltagi (2013), Hsiao (2014) and Pesaran (2015). It is designed for high-level graduate courses in econometrics and statistics. It can be used as a reference for researchers. In specific, Chapters 2 and 4 drew heavily from our published works with Badi H. Baltagi. Chapters 3 and 5 summarize important methods from the recent literature. We would like to thank Badi H. Baltagi for his collaborative work that stimulated our interest in writing this book. We would also like to thank Kunpeng Li for sharing his code, which is used to produce empirical results in Chapter 3. Wei Wang and Mengying Yuan are also acknowledged for helping read the drafts and research assistance. We also wish to thank World Scientific Publishing for giving us the opportunity to undertake this work.

As a personal note, the authors would like to thank their family members. Chihwa thanks his wife Ivy Liu who convinced him of the need for writing this book. Qu wishes to thank his loving wife and parents. The completion of this book would not have been possible without their support.