Probability and Statistics for Economists
eBook - PDF

Probability and Statistics for Economists

Bruce Hansen

  1. 416 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Probability and Statistics for Economists

Bruce Hansen

Book details
Table of contents

About This Book

A comprehensive and up-to-date introduction to the mathematics that all economics students need to know Probability theory is the quantitative language used to handle uncertainty and is the foundation of modern statistics. Probability and Statistics for Economists provides graduate and PhD students with an essential introduction to mathematical probability and statistical theory, which are the basis of the methods used in econometrics. This incisive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of the mathematics that every economist needs to know.

  • Covers probability and statistics with mathematical rigor while emphasizing intuitive explanations that are accessible to economics students of all backgrounds
  • Discusses random variables, parametric and multivariate distributions, sampling, the law of large numbers, central limit theory, maximum likelihood estimation, numerical optimization, hypothesis testing, and more
  • Features hundreds of exercises that enable students to learn by doing
  • Includes an in-depth appendix summarizing important mathematical results as well as a wealth of real-world examples
  • Can serve as a core textbook for a first-semester PhD course in econometrics and as a companion book to Bruce E. Hansen's Econometrics
  • Also an invaluable reference for researchers and practitioners

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Table of contents

  1. Cover
  2. Contents
  3. Preface
  4. Acknowledgments
  5. Mathematical Preparation
  6. Notation
  7. 1. Basic Probability Theory
  8. 2. Random Variables
  9. 3. Parametric Distributions
  10. 4. Multivariate Distributions
  11. 5. Normal and Related Distributions
  12. 6. Sampling
  13. 7. Law of Large Numbers
  14. 8. Central Limit Theory
  15. 9. Advanced Asymptotic Theory
  16. 10. Maximum Likelihood Estimation
  17. 11. Method of Moments
  18. 12. Numerical Optimization
  19. 13. Hypothesis Testing
  20. 14. Confidence Intervals
  21. 15. Shrinkage Estimation
  22. 16. Bayesian Methods
  23. 17. Nonparametric Density Estimation
  24. 18. Empirical Process Theory
  25. Appendix: Mathematics Reference
  26. References
  27. Index