Using SAS for Econometrics
eBook - PDF

Using SAS for Econometrics

  1. 590 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Using SAS for Econometrics

About this book

A supplement such as Using SAS for Econometrics is quite essential for use in a classroom environment, for those attempting to learn SAS, and for quick and useful reference. The SAS documentation comes in many volumes, and several are thousands of pages long. This makes for a very difficult challenge when getting started with SAS. This volume spans several levels of econometrics. It is suitable for undergraduate students who will use canned SAS statistical procedures, and for graduate students who will use advanced procedures as well as direct programming in SAS's matrix language, discussed in chapter appendices. Material within the chapters is accessible to undergraduate and/or Masters students, with appendices to chapters devoted to more advanced materials and matrix programming.

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Yes, you can access Using SAS for Econometrics by R. Carter Hill,Randall C. Campbell in PDF and/or ePUB format, as well as other popular books in Economics & Econometrics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2012
Print ISBN
9781118032091
eBook ISBN
9781118361726
Edition
4
Topic
Economics
Subtopic
Econometrics
Index
Economics

Table of contents

  1. Copyright
  2. PREFACE
  3. BRIEF CONTENTS
  4. CONTENTS
  5. Chapter 1. Introducing SAS
  6. Chapter 2. The Simple Linear Regression Model
  7. Chapter 3. Interval Estimation and Hypothesis Testing
  8. Chapter 4. Prediction, Goodness-of-Fit, and Modeling Issues
  9. Chapter 5. The Multiple Regression Model
  10. Chapter 6. Further Inference in the Multiple Regression Model
  11. Chapter 7. Using Indicator Variables
  12. Chapter 8. Heteroskedasticity
  13. Chapter 9 Regression with Time-Series Data: Stationary Variables
  14. Chapter 10. Random Regressors and Moment-Based Estimation
  15. Chapter 11. Simultaneous Equations Models
  16. Chapter 12. Regression with Time-Series Data: Nonstationary Variables
  17. Chapter 13. Vector Error Correction and Vector Autoregressive Models
  18. Chapter 14. Time-Varying Volatility and ARCH Models
  19. Chapter 15. Panel Data Models
  20. Chapter 16. Qualitative and Limited Dependent Variable Models
  21. Appendix A. Math Functions
  22. Appendix B. Probability
  23. Appendix C. Review of Statistical Inference
  24. Index