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SAS for Econometrics 4 de R. Carter Hill

SAS for Econometrics 4

Autor R. Carter Hill, Randall C. Campbell, Clint Hill
en Limba Engleză Paperback – 1900
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.
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Specificații

ISBN-13: 9781118032091
ISBN-10: 1118032098
Pagini: 592
Dimensiuni: 216 x 280 x 32 mm
Greutate: 1.47 kg
Ediția:4. Auflage
Editura: Wiley
Locul publicării:Hoboken, United States

Public țintă

Finance Professionals, Professors, and Students

Notă biografică

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.

Cuprins

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