Econometrics by Example

The second edition of this bestselling textbook retains its unique learning-by-doing approach to the study of econometrics. Rather than relying on complex theoretical discussions and complicated mathematics, this book explains econometrics from a practical point of view by walking the student through real-life examples, step by step.

The second edition has been fully revised and updated, and features two brand new chapters on Quantile Regression Modeling and Multivariate Regression Models, new extended examples accompanied by real-life data, and new student exercises at the end of each chapter.

Damodar Gujarati is Emeritus Professor of Economics, US Military Academy, West Point, New York, USA. He has over 40 years of teaching and writing experience. As well as his bestselling textbooks he has published many articles in leading economics and statistics journals. He has Visiting Professorships at leading universities in the UK, Australia, Singapore and India.

PART I: BASICS OF LINEAR REGRESSION
1. The Linear Regression Model
2. Functional Forms of Regression Models
3. Qualitative Explanatory Variables Regression Models
PART II: REGRESSION DIAGNOSTICS
4. Regression Diagnostic I: Multicollinearity
5. Regression Diagnostic II: Heteroscedasticity
6. Regression Diagnostic III: Autocorrelation
7. Regression Diagnostic IV: Model Specification Errors
PART III: REGRESSION MODELS WITH CROSS-SECTIONAL DATA
8. Stochastic Regressors and the Method of Instrumental Variables
9. The Logit and Probit Models
10. Multinomial Regression Models
11. Ordinal Regression Models
12. Limited Dependent Variable Regression Models
PART IV: TIME SERIES ECONOMETRICS
13. Modeling Count Data
14. Stationary and Nonstationary Time Series
15. Conintegration and Error Correction Models
16. Asset Price Volatility: the ARCH and GARCH Models
PART V: SELECTED TOPICS IN ECONOMETRICS
17. Economic Forecasting
18. Panel Data Regression Models
19. Stochastic Regressors and the Method of Instrumental Variables
20. Quantile Regression Modeling
21. Multivariate Regression Models