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,
and 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 appendixes
containing more advanced topics and/or difficult proofs. Moreover, there
are three appendixes with material that is supposed to be known. Appendix I contains
a comprehensive reviewof 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.
Herman