Provides an introduction to econometrics, without resorting to matrix algebra, calculus, or statistics beyond the elementary level. In this book, the material covered in the appendices is assigned to students with mathematical bend. They can study matrix algebra given in Appendix B, and linear regression model using matrix algebra in Appendix C.

Gujarati's Basic Econometrics provides an elementary but comprehensive introduction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level. Because of the way the book is organized, it may be used at a variety of levels of rigor; for example, the material covered in the appendices may be assigned to students with mathematical bend. More advanced students can study matrix algebra given in Appendix B, and can then study the linear regression model using matrix algebra in Appendix C. Theoretical exercises marked with asterisks may be covered selectively. Gujarati remains accessible to a wide variety of students, because it covers the material without excessive mathematical rigor or advanced statistics. A disk of data sets is provided with the text.

• 1. The Nature of Regression Analysis. 2. Two-Variable Regression Analysis: Some Basic Ideas. 3. Two Variable Regression Model: The Problem of Estimation. 4. The Normality Assumption: Classical Normal Linear Regression Model. 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. Regression on Dummy variables. 10. Multicollinearity: What happens if the Regressor are correlated. 11. Heteroscedasticity. 12. Autocorrelation. 13. Econometric Modeling I: Traditional Econometric Methodology. 14. Econometric Modeling II: Alternative Econometric Methodologies. 15. Regression on Dummy Dependent Variable: The LPM, Probit, and Tobit Models16. Nominal Ordinal and other Limited Dependent Variable regression models. . 17. Dynamic Econometric Model: Autoregressive and Distributed Lag Models. 18. Simultaneous-Equation Models. 19. The Identification Problem. 20. Simultaneous-Equation Methods. 21. Time Series Econometrics I: Stationarity, Unit Roots, and Cointegration. 22. Time Series Econometrics II: ARIMA and VAR Models.23. Non-linear in the Parameter Regression Models24. Panel Data Regression Models AppendixesA. A Review of Some Statistical ConceptsB. Rudiments of Matrix AlgebraC. Linear Regression Model in Matrix NotationD. Statistical TablesSelected BibliographyIndexes Name Index Subject Index