Analysis of Panel Data

by Cheng Hsiao

Overview

Now in its fourth edition, this comprehensive introduction of fundamental panel data methodologies provides insights on what is most essential in panel literature. A capstone to the forty-year career of a pioneer of panel data analysis, this new edition's primary contribution will be the coverage of advancements in panel data analysis, a statistical method widely used to analyze two or higher-dimensional panel data. The topics discussed in early editions have been reorganized and streamlined to comprehensively introduce panel econometric methodologies useful for identifying causal relationships among variables, supported by interdisciplinary examples and case studies. This book, to be featured in Cambridge's Econometric Society Monographs series, has been the leader in the field since the first edition. It is essential reading for researchers, practitioners and graduate students interested in the analysis of microeconomic behavior.

Product Details

ISBN-13:	9781009060752
Publisher:	Cambridge University Press
Publication date:	07/07/2022
Series:	Econometric Society Monographs
Edition description:	4th ed.
Pages:	500
Product dimensions:	6.61(w) x 9.57(h) x 1.10(d)

About the Author

Cheng Hsiao is Professor of Economics at the University of Southern California.

Table of Contents

1. Introduction; 2. Homogeneity test for linear regression models (analysis of covariance); 3. Simple regression with variable intercepts; 4. Dynamic models with variable intercepts; 5. Simultaneous-equations models; 6. Variable-coefficient models; 7. Discrete data; 8. Truncated and censored data; 9. Cross-sectional dependent panel data; 10. Dynamic system; 11. Incomplete panel data; 12. Miscellaneous topics; 13. A summary view.

Editorial Reviews

Review of previous edition:
"Researchers will find that the insights that they gain from working through the book's tougher sections are well worth the effort. The book remains an indispensable and comprehensive reference for panel estimation methods."
David C. Ribar, International Journal of Forecasting

From the Publisher