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. Some of the strengths of this book lie in presenting difficult material in a simple, yet rigorous manner. Each chapter has a set of theoretical exercises as well as an empirical illustration using a real economic application. These empirical exercises usually replicate a published article using Stata or Eviews.

"Badi H. Baltagi is distinguished Professor of Economics, and Senior Research Associate at the Center for Policy Research, Syracuse University. He received his Ph.D. in Economics at the University of Pennsylvania in 1979. Before joining Syracuse University, he served on the faculty at the University of Houston and Texas A&M University. He is a fellow of the Journal of Econometrics and a recipient of the Multa and Plura Scripsit Awards from Econometric Theory."

Klappentext

Here at last is the fourth edition of the textbook that is required reading for economics students as well as those practising applied economics. Not only does it teach some of the basic econometric methods and the underlying assumptions behind them, but it also includes a simple and concise treatment of more advanced topics from spatial correlation to time series analysis. This book's strength lies in its ability to present complex material in a simple, yet rigorous manner. This superb fourth edition updates identification and estimation methods in the simultaneous equation model. It also reviews the problem of weak instrumental variables as well as updating panel data methods.

Inhalt

I.- What Is Econometrics?.- Basic Statistical Concepts.- Simple Linear Regression.- Multiple Regression Analysis.- Violations of the Classical Assumptions.- Distributed Lags and Dynamic Models.- II.- 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.