‘Econometric Analysis of Panel Data’ has become established as the leading textbook for postgraduate courses in panel data. This book is intended as a companion to the main text. The prerequisites include a good background in mathematical statistics and econometrics. The companion guide will add value to the existing textbooks on panel data by solving exercises in a logical and pedagogical manner, helping the reader understand, learn and teach panel data. These exercises are based upon those in Baltagi (2008) and are complementary to that text even though they are stand alone material and the reader can learn the basic material as they go through these exercises. The exercises in this book start by providing some background material on partitioned regressions and the Frisch-Waugh-Lovell theorem, showing the reader some applications of this material that are useful in practice. Then it goes through the basic material on fixed and random effects models in a one-way and two-way error components models, following the same outline as in Baltagi (2008). The book also provides some empirical illustrations and examples using Stata and EViews that the reader can replicate. The data sets are available on the Wiley web site (www.wileyeurope.com/college/baltagi).

Econometric Analysis of Panel Data has become established as one of the leading textbooks for students of panel data. The significantly revised and updated third edition from one of the leading researchers and writers in this field builds upon the success of previous editions, and includes the most recent empirical examples from panel data literature. Updated topics include dynamic panels, nonstationary panels, limited dependent variable models, heteroskedastic panels, heterogeneous panels and spatial panels. Other notable features of this third edition: The chapter on nonstationary panels has been completely rewritten and updated to include the recent unit root panel tests with cross-section dependence, and an empirical application is given on purchasing power parity, which is illustrated using Eviews. An empirical example on nursing labor supply has been added, illustrating limited dependent variables methods with panel data. Additional exercises have been added to each chapter and their solutions will be provided on the website. TSP, EViews and Stata output examples are given throughout the book. A simultaneous equation on crime has been added and is illustrated with Stata. Material on heteroske4dasticity in panels is completely revised and updated with recent estimation and testing results.

In the last 20 years, econometric theory on panel data has developed rapidly, particularly for analyzing common behaviors among individuals over time. Meanwhile, the statistical methods employed by applied researchers have not kept up-to-date. This book attempts to fill in this gap by teaching researchers how to use the latest panel estimation methods correctly. Almost all applied economics articles use panel data or panel regressions. However, many empirical results from typical panel data analyses are not correctly executed. This book aims to help applied researchers to run panel regressions correctly and avoid common mistakes. The book explains how to model cross-sectional dependence, how to estimate a few key common variables, and how to identify them. It also provides guidance on how to separate out the long-run relationship and common dynamic and idiosyncratic dynamic relationships from a set of panel data. Aimed at applied researchers who want to learn about panel data econometrics by running statistical software, this book provides clear guidance and is supported by a full range of online teaching and learning materials. It includes practice sections on MATLAB, STATA, and GAUSS throughout, along with short and simple econometric theories on basic panel regressions for those who are unfamiliar with econometric theory on traditional panel regressions.

A Companion to Theoretical Econometrics provides a comprehensive reference to the basics of econometrics. This companion focuses on the foundations of the field and at the same time integrates popular topics often encountered by practitioners. The chapters are written by international experts and provide up-to-date research in areas not usually covered by standard econometric texts. Focuses on the foundations of econometrics. Integrates real-world topics encountered by professionals and practitioners. Draws on up-to-date research in areas not covered by standard econometrics texts. Organized to provide clear, accessible information and point to further readings.

This is the essential companion to the second edition of Jeffrey Wooldridge's widely used graduate econometrics text. The text provides an intuitive but rigorous treatment of two state-of-the-art methods used in contemporary microeconomic research. The numerous end-of-chapter exercises are an important component of the book, encouraging the student to use and extend the analytic methods presented in the book. This manual contains advice for answering selected problems, new examples, and supplementary materials designed by the author, which work together to enhance the benefits of the text. Users of the textbook will find the manual a necessary adjunct to the book.

The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

Written by one of the world's leading researchers and writers in the field, Econometric Analysis of Panel Data has become established as the leading textbook for postgraduate courses in panel data. This new edition reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. Featuring the most recent empirical examples from panel data literature, data sets are also provided as well as the programs to implement the estimation and testing procedures described in the book. These programs will be made available via an accompanying website which will also contain solutions to end of chapter exercises that will appear in the book. The text has been fully updated with new material on dynamic panel data models and recent results on non-linear panel models and in particular work on limited dependent variables panel data models.