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Introduction to the Mathematical and Statistical Foundations of Econometrics (Themes in Modern Econometrics) (Paperback)
by Herman J. Bierens (Author)
Key Phrases: maximum likelihood theory, lotto case, uniform weak law, Prove Theorem, Modes of Convergence, Mathematical Expectations
Editorial Reviews
Review
'The objective of this book is to use it as an introductory text for a Ph.D. level course in Econometrics. ... Appendixes are self contained with review which are easy to learn and understand. As a whole, I consider this book as unique and self-contained and it will be a great resource for researchers in the area of Econometrics.' Zentralblatt MATH
Product Description
This book is intended for use in a rigorous introductory Ph.D. 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.
'The objective of this book is to use it as an introductory text for a Ph.D. level course in Econometrics. ... Appendixes are self contained with review which are easy to learn and understand. As a whole, I consider this book as unique and self-contained and it will be a great resource for researchers in the area of Econometrics.' Zentralblatt MATH
Product Description
This book is intended for use in a rigorous introductory Ph.D. 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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