on $25 & up

ISBN13: 978-0072478525

ISBN10: 0072478527 Edition: 4TH 03

Copyright: 2003

Publisher: Richard D. Irwin, Inc.

Published: 2003

International: No

ISBN10: 0072478527 Edition: 4TH 03

Copyright: 2003

Publisher: Richard D. Irwin, Inc.

Published: 2003

International: No

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, if matrix algebra is used, theoretical exercises may be omitted. A CD of data sets is provided with the text.

New to This Edition :

- Matrix approach to linear regression condensed: To make the book more accessible to the non-specialist, the discussion of the matrix approach to linear regression has been moved from old Chapter 9 to Appendix C. Appendix C is slightly expanded to include some advanced material for the benefit of the more mathematically inclined students.
- Chapters on Econometric Modeling Improved and Condensed: Chapter 13 on econometric modeling replaces old chapters 13 and 14. This chapter has several new topics that the applied researcher will find particularly useful. These include a compact discussion of model selection criteria, such as Akaike information criterion, Schwarz information criterion, Mallow's Cp criterion, and forecast chi-square.
- New chapter on Nonlinear Regression Models: Chapter 14 on nonlinear regression models is new. Because of the easy availability of statistical software, it is no longer difficult to estimate regression models that are nonlinear in the parameters. Some econometric models are intrinsically nonlinear in the parameters and need to be estimated by iterative methods. This chapter discusses and illustrates some comparatively simple methods of estimating nonlinear-in-parameter regression models.
- New material on Panel Data Regression Models: Chapter 16 on panel data regression models is new. Panel data combines features of both time series and cross-section data. Because of increasing availability of panel data in the social sciences, panel data regression models are being increasingly used by researchers in many fields. This chapter provides a non-technical discussion of the fixed effects and random effects models that are commonly used in estimating regression models based on panel data.
- Appendix A on statistical concepts has been expanded. Appendix C discusses the linear regression model using matrix algebra. This is for the benefit of the more advanced students as it provides more challenged and advanced discussion and practice.
- Contains 50 new data sets dealing with actual economic data drawn from both economics and other social sciences. These new data sets will keep the material fresh, and because they are drawn from actual sources, underscore the relevance of the material covered.
- Extensive information about websites relating to economic data has been added. The Internet is an increasingly important tool in analyzing economic data, and this new feature aids students in doing econometric research via the Web.
- Eviews software can now be packaged with
*Basic Econometrics*at a discount to students.

Features :

*Basic Econometrics*gives a comprehensive introduction to econometrics, but remains accessible to a wide variety of students because it covers the material without excessive mathematical rigor or statistics. Appropriate for those students who have only a basic understanding of algebra and statistics.- Concrete examples from a variety of disciplines: As in the previous editions, all the econometric techniques discussed in this book are illustrated by examples, several of which are based on concrete data from various disciplines.
- Data CD included with book. The data CD contains all numerical data that are included in the chapters to illustrate various econometric techniques as well as the numerical data included in the Problem section of the end of chapter Exercises. All the data are in ASCII format and can be easily imported into statistical packages, such as Minitab, EVIEWS, SHAZAM, LIMDEP, RATS, SPSS, and STATA. This CD allows the students to learn by doing; a necessity in the study of econometrics.

Author Bio

**Gujarati, Damodar N. : US Military Academy **

Introduction

Part I : 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 Assumptions of the Classical Model

10. Multicollinearity: What Happens if the Regressions are Correlated?

11. Heteroscedasticity: What Happens if the Error Variance is Nonconstant?

12. Autocorrelation: What Happens if the Error Terms are Correlated?

13. Econometric Modeling I: 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 Model: Autoregressive and Distributed Lag Models

Part 4: Simultaneous Equation Models

18. Simultaneous-Equation Models

19. The Identification Problem

20. Simultaneous-Equation Methods

Part 5: Time Series Econometrics

21. Time Series Econometrics: Some Basic Concepts

22. Time Series Econometrics: Forecasting

Appendixes

A. A Review of Some Statistical Concepts

B. Rudiments of Matrix Algebra

C. The Matrix Approach to the Linear Regression Model

D. Statistical Tables

Selected Bibliography

Indexes

Name Index

Subject Index

ISBN10: 0072478527 Edition: 4TH 03

Copyright: 2003

Publisher: Richard D. Irwin, Inc.

Published: 2003

International: No

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, if matrix algebra is used, theoretical exercises may be omitted. A CD of data sets is provided with the text.

New to This Edition :

- Matrix approach to linear regression condensed: To make the book more accessible to the non-specialist, the discussion of the matrix approach to linear regression has been moved from old Chapter 9 to Appendix C. Appendix C is slightly expanded to include some advanced material for the benefit of the more mathematically inclined students.
- Chapters on Econometric Modeling Improved and Condensed: Chapter 13 on econometric modeling replaces old chapters 13 and 14. This chapter has several new topics that the applied researcher will find particularly useful. These include a compact discussion of model selection criteria, such as Akaike information criterion, Schwarz information criterion, Mallow's Cp criterion, and forecast chi-square.
- New chapter on Nonlinear Regression Models: Chapter 14 on nonlinear regression models is new. Because of the easy availability of statistical software, it is no longer difficult to estimate regression models that are nonlinear in the parameters. Some econometric models are intrinsically nonlinear in the parameters and need to be estimated by iterative methods. This chapter discusses and illustrates some comparatively simple methods of estimating nonlinear-in-parameter regression models.
- New material on Panel Data Regression Models: Chapter 16 on panel data regression models is new. Panel data combines features of both time series and cross-section data. Because of increasing availability of panel data in the social sciences, panel data regression models are being increasingly used by researchers in many fields. This chapter provides a non-technical discussion of the fixed effects and random effects models that are commonly used in estimating regression models based on panel data.
- Appendix A on statistical concepts has been expanded. Appendix C discusses the linear regression model using matrix algebra. This is for the benefit of the more advanced students as it provides more challenged and advanced discussion and practice.
- Contains 50 new data sets dealing with actual economic data drawn from both economics and other social sciences. These new data sets will keep the material fresh, and because they are drawn from actual sources, underscore the relevance of the material covered.
- Extensive information about websites relating to economic data has been added. The Internet is an increasingly important tool in analyzing economic data, and this new feature aids students in doing econometric research via the Web.
- Eviews software can now be packaged with
*Basic Econometrics*at a discount to students.

Features :

*Basic Econometrics*gives a comprehensive introduction to econometrics, but remains accessible to a wide variety of students because it covers the material without excessive mathematical rigor or statistics. Appropriate for those students who have only a basic understanding of algebra and statistics.- Concrete examples from a variety of disciplines: As in the previous editions, all the econometric techniques discussed in this book are illustrated by examples, several of which are based on concrete data from various disciplines.
- Data CD included with book. The data CD contains all numerical data that are included in the chapters to illustrate various econometric techniques as well as the numerical data included in the Problem section of the end of chapter Exercises. All the data are in ASCII format and can be easily imported into statistical packages, such as Minitab, EVIEWS, SHAZAM, LIMDEP, RATS, SPSS, and STATA. This CD allows the students to learn by doing; a necessity in the study of econometrics.

Author Bio

**Gujarati, Damodar N. : US Military Academy **

Table of Contents

Introduction

Part I : 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 Assumptions of the Classical Model

10. Multicollinearity: What Happens if the Regressions are Correlated?

11. Heteroscedasticity: What Happens if the Error Variance is Nonconstant?

12. Autocorrelation: What Happens if the Error Terms are Correlated?

13. Econometric Modeling I: 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 Model: Autoregressive and Distributed Lag Models

Part 4: Simultaneous Equation Models

18. Simultaneous-Equation Models

19. The Identification Problem

20. Simultaneous-Equation Methods

Part 5: Time Series Econometrics

21. Time Series Econometrics: Some Basic Concepts

22. Time Series Econometrics: Forecasting

Appendixes

A. A Review of Some Statistical Concepts

B. Rudiments of Matrix Algebra

C. The Matrix Approach to the Linear Regression Model

D. Statistical Tables

Selected Bibliography

Indexes

Name Index

Subject Index

- Marketplace
- From