Basic econometrics
- 6th
- New Delhi McGraw Hill Education (India) Pvt. Ltd. 2021
- xxiv, 910 p.
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 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