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Analysis of Panel Data, Fourth Edition


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Author:
Cheng Hsiao
Publisher: Cambridge University Press
Copyright: 2022
ISBN-13: 978-1-316-51210-4
Pages: 500; paperback
Author:
Cheng Hsiao
Publisher: Cambridge University Press
Copyright: 2022
ISBN-13:
Pages: 500; eBook
Price: $0.00
Author:
Cheng Hsiao
Publisher: Cambridge University Press
Copyright: 2022
ISBN-13:
Pages: 500; Kindle
Price: $

Comment from the Stata technical group

Cheng Hsiao's Analysis of Panel Data, Fourth Edition is an essential reference on panel-data models. The fourth edition is a minor but important revision; like the previous three editions, it is a must-have reference book for researchers and graduate students.

The fourth edition retains most of the content from the previous edition, although some chapters were reordered. The first two chapters of the book provide detailed introductions to static random- and fixed-effects models, including model estimation, specification testing, and treatment of heteroskedasticity and correlation.

Chapter 3 is a concise overview of dynamic panel-data models, including the GMM estimators that have become quite popular in recent years, and Hsiao does a superb job of bringing the literature together into one cohesive discussion. Estimation of static simultaneous-equation models is covered in chapter 4.

Chapter 5 covers dynamic panel-data systems, including vector autoregressive models, conintegrated models, and time-series-related tests. Chapters 6--8 discuss parametric and semiparametric approaches to limited dependent-variable models and provide many references to the growing literature in this field. Particularly, chapter 6 includes a clear presentation of the incidental parameters problem when it comes to nonlinear panel estimation with individual effects. Hsiao does an excellent job of covering these essential topics.

Chapter 9 presents a smorgasbord of further topics, including simulation, multilevel models, pseudopanels, and missing data. The section is rather brief, but it does provide many references to the relevant literature. Chapter 10 adds new topics, such as mixed effects and factor dimension determination.

Chapter 11 covers cross-sectionally dependent data. Chapter 12 provides tools for evaluating treatment for both cross-sectional and panel data. Chapter 13 is devoted to variable-coefficient models, including Swamy's random-coefficients model and its relatives: fixed-coefficients models and their dynamic counterparts.

Chapter 14 is new to this edition and makes the latest edition more suitable for recent challenges regarding analyzing big and high-dimensional data and using machine learning algorithms.

In short, the fourth edition of Analysis of Panel Data, just like the first three editions, will prove to be an invaluable reference to users of panel data.

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