Analysis of Panel Data
This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined. Other new material includes correlated random coefficient models, pseudo-panels, duration and count data models, quantile analysis, and alternative approaches for controlling the impact of unobserved heterogeneity in nonlinear panel data models.
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Homogeneity Tests for Linear Regression Models
Simple Regression with Variable Intercepts
Dynamic Models with Variable Intercepts
Large N and T Asymptotics
Static SimultaneousEquations Models
Sample Truncation and Sample Selection
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ANCOVA approach assume assumption asymptotically normally asymptotically normally distributed Bayes estimator bias Chapter cointegration common conditional consistent and asymptotically consistent estimator constant converges covariance matrix cross-sectional data cross-sectional dependence cross-sectional units CV estimator degrees of freedom denotes density dependent variable discussed dynamic efficient equation error term exogenous variables explanatory variables finite fixed-effects given GLS estimator Heckman heterogeneity Hsiao incidental parameters individual effects individual-specific effects inference intercept Kyriazidou lag coefficients least-squares estimator likelihood function likelihood-ratio test linear logit model method normally distributed null obtain panel data Pesaran Prob probability random variable random-effects random-effects model regression model residuals restrictions sample Section serial correlation specific structure tends to infinity test statistic transformed truncated uncorrelated unit root unobserved variance variance–covariance matrix vector yields yyio