Econometric Analysis of Cross Section and Panel Data
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.
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2SLS 2SLS estimator 3SLS a¤ect allow analysis APEs apply approach assume assumption asymptotic variance asymptotically normal average binary response bootstrap Chapter coe‰cient compute conditional mean consistently estimate corner solution covariates cross section deﬁne denote density depends di‰cult di¤erent discussed dummies e‰cient EðyjxÞ esti example exogenous variables explanatory variables FE estimator ffiffiffiffiffi FGLS fixed e¤ects fully robust g ¼ heteroskedasticity homoskedasticity independent instrumental variables jxiÞ least squares linear model linear projection log likelihood logit M-estimator methods nonlinear null hypothesis observed obtain OLS estimator ordered probit panel data parameters partial e¤ects Poisson pooled OLS population probit model problem propensity score QMLE random e¤ects random sample rank condition reduced form regression regressors residuals restrictions serial correlation simple standard errors strict exogeneity su‰cient Theorem tion Tobit two-step uncorrelated unobserved e¤ects usual values Wooldridge XN i¼1 y2 ¼ zero