This acclaimed graduate textbook, Wooldridge’s Econometric Analysis of Cross Section and Panel Data 2nd edition (PDF) provides a unified treatment of 2 methods used in contemporary econometric research: data panel and cross-section methods. By focusing on assumptions that can be given behavioral content, the ebook maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both nonlinear and linear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific nonlinear and linear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, causal (or treatment) effects, censored and missing data schemes, and duration analysis.
Econometric Analysis of Cross Section and Panel Data 2e was the first graduate econometrics textbook to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This 2nd edition has been substantially revised and updated. Improvements include a broader class of models for missing data problems; expanded discussion of “generalized instrumental variables” (GIV) estimation; more detailed treatment of cluster problems, an important topic for empirical researchers; 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 students what does work but why certain “obvious” procedures don’t. The numerous included exercises, both theoretical and computer-based, allow the students to extend methods covered in the textbook and discover new insights.
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