Using SAS for Econometrics

by Hill, R. Carter; Campbell, Randall C.

• ISBN13: 9781118032091
• ISBN10: 1118032098
• Edition: 4th
• Format: Paperback
• Copyright: 2011-09-21
• Publisher: Wiley

Summary

Principles of Econometrics, 4th Edition, is an introductory book on economics and finance designed to provide an understanding of why econometrics is necessary, and a working knowledge of basic econometric tools. This latest edition is updated to reflect current state of economic and financial markets and provides new content on Kernel Density Fitting and Analysis of Treatment Effects. It offers new end-of-chapters questions and problems in each chapter; updated comprehensive Glossary of Terms; and summary of Probably and Statistics. The text applies basic econometric tools to modeling, estimation, inference, and forecasting through real world problems and evaluates critically the results and conclusions from others who use basic econometric tools. Furthermore, it provides a foundation and understanding for further study of econometrics and more advanced techniques.

Author Biography

Dr. Hill is a professor of economics at Louisiana State University. He received his bachelors from Duke University and his doctorate from University of Missouri-Columbia. He has published a wealth of journal articles, papers in books and dissertations. Dr. Hill served as a consultant to AT&T, Georgia Power Company, and Southern Company of Birmingham. He has received over a dozen commendations from Louisiana State and other institutions for excellence in teaching.

Table of Contents

1. Introducing SAS 1

2. The Simple Linear Regression Model 50

3. Interval Estimation and Hypothesis Testing 82

4. Prediction, Goodness-of-Fit, and Modeling Issues 103

5. The Multiple Regression Model 130

6. Further Inference in the Multiple Regression Model 162

7. Using Indicator Variables 190

8. Heteroskedasticity 207

9. Regression with Time-Series Data: Stationary Variables 264

10. Random Regressors and Moment-Based Estimation 304

11. Simultaneous Equations Models 346

12. Regression with Time-Series Data: Nonstationary Variables 369

13. Vector Error Correction and Vector Autoregressive Models 390

14. Time-Varying Volatility and ARCH Models 406

15. Panel Data Models 428

16. Qualitative and Limited Dependent Variable Models 468

Appendix A. Math Functions 522

Appendix B. Probability 528

Appendix C. Review of Statistical Inference 541