Introduction to the Mathematical and Statistical Foundations of Econometrics
eBook - PDF

Introduction to the Mathematical and Statistical Foundations of Econometrics

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

Introduction to the Mathematical and Statistical Foundations of Econometrics

About this book

This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit theorems and related results for independent random variables as well as for stationary time series, with applications to asymptotic inference of M-estimators, and maximum likelihood theory. Some chapters have their own appendices containing the more advanced topics and/or difficult proofs. Moreover, there are three appendices with material that is supposed to be known. Appendix I contains a comprehensive review of linear algebra, including all the proofs. Appendix II reviews a variety of mathematical topics and concepts that are used throughout the main text, and Appendix III reviews complex analysis. Therefore, this book is uniquely self-contained.

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Yes, you can access Introduction to the Mathematical and Statistical Foundations of Econometrics by Herman J. Bierens 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
Cambridge University Press
Year
2004
Print ISBN
9780521834315
eBook ISBN
9780511078897
Topic
Economics
Subtopic
Econometrics
Index
Economics

Table of contents

  1. Cover
  2. Half-title
  3. Series-title
  4. Title
  5. Copyright
  6. Contents
  7. Preface
  8. 1 Probability and Measure
  9. 2 Borel Measurability, Integration, and Mathematical Expectations
  10. 3 Conditional Expectations
  11. 4 Distributions and Transformations
  12. 5 The Multivariate Normal Distribution and Its Application to Statistical Inference
  13. 6 Modes of Convergence
  14. 7 Dependent Laws of Large Numbers and Central Limit Theorems
  15. 8 Maximum Likelihood Theory
  16. Appendix I – Review of Linear Algebr
  17. Appendix II – Miscellaneous Mathematics
  18. Appendix III – A Brief Review of Complex Analysis
  19. Appendix IV – Tables of Critical Values
  20. References
  21. Index