Gujarati and Porters Basic Econometrics provides an elementary but comprehensive introduction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level. With the addition of over 100 new data sets, as well as significantly updated research and examples, the Fifth Edition responds to important developments in the theory and practice of econometrics. Basic Econometrics is widely used by students of all fields as the expanded topics and concrete applications throughout the text apply to a broad range of studies.
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
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
Appendix F: Economic Data on the World Wide Web
New to this Edition
New and Updated Data: All of the data used in the illustrative examples has been updated. Over 100 new data sets are available in the Fifth Edition. Several new examples specific to chapter content have also been updated throughout the textbook. Small tables of data are included in the book, but large sample data are posted on the book's website.
Several examples of concrete computer printouts: Several examples are supplemented by results from EViews (version 6) and STATA (version 10), as well as MINITAB (version 15).
Class Exercises: In a few chapters, new class exercises encourage students to obtain their own data and implement the various techniques discussed in the book. This edition also incorporates Monte Carlo simulations.
Extended concluding examples: In several chapters, the authors have added extended concluding examples to illustrate the various points made in the text and to make the most current material available to students.
Revitalized design: A new two-color design highlights the latest diagrams, graphs, and new data-based exercises.
Reorganization of Chapter 7: Chapter 7 now discusses not only the marginal impact of a single regressor on the dependent variable but also the impacts of simultaneous changes of all the explanatory variables on the dependent variable.
New Discussion in Chapter 12: There is new discussion of the impact of structural breaks on autocorrelation in Chapter 12.
New topics in Chapter 13: New topics included in Chapter 13 are missing data, non-normal error term, and stochastic, or random, regressors.
Several new examples in Chapter 15: Chapter 15 contains several new examples that illustrate the use of logit and probit models in various fields.
Revised discussion on panel data regression models: Chapter 16 on panel data regression models has been thoroughly revised and illustrated with several applications.
Sims and Granger causality tests: An extended discussion of Sims and Granger causality tests is now included in Chapter 17.
Stationary and non-stationary time series: Stationary and non-stationary time series, as well as some of the problems associated with various tests of stationarity, are now thoroughly discussed in Chapter 21.
New discussion in Chapter 22: Chapter 22 includes a discussion on why taking the first differences of a time series for the purpose of making it stationary may not be the appropriate strategy in some situations.
User-Friendly Approach: Basic Econometrics retains its user-friendly approach to the major topics in econometrics. The discussion is technically focused but readable with current research and data presented in a clear format. Anyone with a basic understanding of introductory statistics and college level mathematics will be able to understand the topics discussed in the text.
Appendixes: The inherently technical material still remains in the appendices of the textbook. This lessens the rigor of the textbook to allow students a more intuitive understanding of the material.
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