Econometrics by Example

The second edition of this bestselling textbook retains its unique learning-by-doing approach to 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. Damodar Gujarati s  clear, concise, writing style guides students from model formulation, to estimation and hypothesis-testing, through to post-estimation diagnostics. The basic statistics needed to follow the book are covered in an appendix, making the book a flexible and self-contained learning resource.  

The textbook is ideal for undergraduate students in economics, business, marketing, finance, operations research and related disciplines. It is also intended for students in MBA programs across the social sciences, and for researchers in business, government and research organizations who require econometrics. 

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.