Basic econometrics

Preface Acknowledge Introduction Part 1 Single-Equation Regression Models 1. The Nature of Regression Analysis 2. Two-Variable Regression Analysis: Some Basic Ideas 3. Two-Variable Regression Model: The Problem of Estimation 4. Classical Normal Linear Regression Model (CNLRM) 5. Two-Variable Regression: Interval Estimation and Hypothesis Testing 6. Extensions of the Two-Variable Linear Regression Model 7. Multiple Regression Analysis: The Problem of Estimation 8. Multiple Regression Analysis: The Problem of Inference 9. Dummy Variable Regression Models Part 2 Relaxing the Assumptions of the Classical Model 10. Multicollinearity: What Happens if the Regressors are Correlated? 11. Heteroscedasticity: What Happens if the Error Variance is Noneonstant? 12. Autocorrelation: What Happens if the Error Terms are Correlate? 13. Econometric Modeling: Model Specification and Diagnostic Testing Part 3 Topics in Econometrics 14. Nonlinear Regression Models 15. Qualitative Response Regression Models 16. Panel Data Regression Models 17. Dynamic Econometric Models: Autoregressive and Distributed-Lag Models Part 4 Simultaneous-Equation Models and Time Series Econometrics 18. Simultaneous-Equation Models 19. The Identification Problem 20. Simultaneous-Equation Methods 21. Time Series Econometrics: Some Basic Concepts 22. Time Series Econometrics: Forecasting Appendix D Statistical Tables Selected Bibliography Index

The fifth edition of Basic Econometrics continues to blend foundations of econometrics with up-to-date research. It illustrates important concepts through intuitive and informative examples & data without resorting to matrix algebra, calculus or statistics beyond the elementary level. It presents not only the ‘what’ and the ‘how’ of econometrics, but also the ‘why’ and successfully provides thorough yet highly lucid descriptions of all the key econometric topics.