This textbook teaches some of the basic econometric methods and the underlying assumptions behind them. It also includes a simple and concise treatment of more advanced topics in spatial correlation, panel data, limited dependent variables, regression diagnostics, specification testing and time series analysis. Each chapter has a set of theoretical exercises as well as empirical illustrations using real economic applications. These empirical exercises usually replicate a published article using Stata, Eviews as well as SAS.

This new sixth edition has been fully revised and updated, and includes new material on limited dependent variables and panel data as well as revision of basic topics like heteroskedasticity, endogeneity, over-identification and specification testing. The author also provides more exercises and empirical examples based on published economic applications.

6th revised and extended edition Offers a useful introduction and solid training in Econometrics Teaches econometric methods for estimating, testing, and forecasting Illustrates methods with practical software including Stata and EViews

Autorentext
Badi H. Baltagi is a Distinguished Professor of Economics and Senior Research Associate at the Center for Policy Research, The Maxwell School, Syracuse University (USA). Professor Baltagi is the current editor of Economics Letters, having previously served as the editor of Empirical Economics (1999-2018) and as the replication editor for the Journal of Applied Econometrics (2003-2018). He is a fellow of the Journal of Econometrics as well as Econometric Reviews and a recipient of the Multa and Plura Scripsit Awards from Econometric Theory, as well as the Distinguished Authors Award from the Journal of Applied Econometrics.

Inhalt
Part 1: What Is Econometrics?.- Basic Statistical Concepts.- Simple Linear Regression.- Multiple Regression Analysis.- Violations of the Classical Assumptions.- Distributed Lags and Dynamic Models.- Part 2: The General Linear Model: The Basics.- Regression Diagnostics and Specification Tests.- Generalized Least Squares.- Seemingly Unrelated Regressions.- Simultaneous Equations Model.- Pooling Time-Series of Cross-Section Data.- Limited Dependent Variables.- Time-Series Analysis.