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Economics - Econometrics / Statistics | Econometrics


Baltagi, Badi H.

4th ed. 2008


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  • Useful introduction to, and solid training in, econometrics
  • Complete with applications and hands-on exercises
  • Provides econometric methods for estimating, testing, and forecasting to applied economists and social scientists
  • Illustrates methods with practical software including Stata and EViews

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."


Content Level » Graduate

Keywords » Microeconometrics - Multiple Regression - Panel Data - Regression analysis - Stata - Time Series Analysis - Time series - calculus - correlation - econometrics - linear regression

Related subjects » Applications - Econometrics / Statistics - Game Theory / Mathematical Methods - Social Sciences & Law

Table of contents 

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.

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