Basic econometrics

TABLE OF CONTENTS
Introduction


Part I: Single-Equation Regression Model


Chapter 1: The Nature of Regression Analysis
Chapter 2: Two-Variable Regression Analysis: Some Basic Ideas
Chapter 3: Two Variable Regression Model: The Problem of Estimation
Chapter 4: Classical Normal Linear Regression Model (CNLRM
Chapter 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing
Chapter 6: Extensions of the Two-Variable Linear Regression Model
Chapter 7: Multiple Regression Analysis: The Problem of Estimation
Chapter 8: Multiple Regression Analysis: The Problem of Inference
Chapter 9: Dummy Variable Regression Models


Part II: Relaxing the Assumptions of the Classical Model


Chapter 10: Multicollinearity: What happens if the Regressor are Correlated
Chapter 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant?
Chapter 12: Autocorrelation: What Happens if the Error Terms are Correlated
Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing


Part III: Topics in Econometrics


Chapter 14: Nonlinear Regression Models
Chapter 15: Qualitative Response Regression Models
Chapter 16: Panel Data Regression Models
Chapter 17: Dynamic Econometric Model: Autoregressive and Distributed-Lag Models


Part IV: Simultaneous-Equation Models and Time Series Econometrics


Chapter 18: Simultaneous-Equation Models.
Chapter 19: The Identification Problem.
Chapter 20: Simultaneous-Equation Methods.
Chapter 21: Time Series Econometrics: Some Basic Concepts
Chapter 22: Time Series Econometrics: Forecasting


Appendix A: Review of Some Statistical Concepts
Appendix B: Rudiments of Matrix Algebra
Appendix C: The Matrix Approach to Linear Regression Model
Appendix D: Statistical Tables
Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA


Online Content:
Appendix F: Economic Data on the World Wide Web
Selected Bibliography
Index

OVERVIEW


In its sixth edition, Basic Econometrics is a thoroughly revised text that introduces readers to the fundamentals of the subject. All major latest state-of-the-art topics have been added without compromising on the lucidity of the text. The book starts by introducing econometrics to the readers and then goes on to discuss single-equation regression models, relaxing the assumptions of the classical model, and various specific topics on econometrics. The book concludes with a detailed discussion on simultaneous-equation models and time series econometrics


KEY FEATURES


• Substantially enhanced sections on time series models and other topics such as normalization, LIMLE method, ergodicity, etc. to meet the Indian curricula requirements
• Inclusion of new topics such as relative contribution of regressors, identification under homogeneous linear restrictions, higher order consistent moment estimators of the regression coefficients, etc.
• Expanded sections on multicollinearity, heteroscedastic error, nonlinear regression techniques, Koyck model, etc.
• Updated and Indianized data across the chapters including the complete table of panel data
• Supplemented with online datasets