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,Introduction to the Mathematical and Statistical Foundations
of Econometrics
This book is intended for use in a rigorous introductory Ph.D.-level course in econo-
metrics or in a field course in econometric theory. It covers the measure–theoretical
foundation of probability theory, the multivariate normal distribution with its ap-
plication 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 ap-
pendixes containing more advanced topics and/or difficult proofs. Moreover, there
are three appendixes with material that is supposed to be known. Appendix I con-
tains 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.
Herman J. Bierens is Professor of Economics at the Pennsylvania State Univer-
sity and part-time Professor of Econometrics at Tilburg University, The Nether-
lands. He is Associate Editor of the Journal of Econometrics and Econometric
Reviews, and has been an Associate Editor of Econometrica. Professor Bierens
has written two monographs, Robust Methods and Asymptotic Theory in Nonlin-
ear Econometrics and Topics in Advanced Econometrics (Cambridge University
Press 1994), as well as numerous journal articles. His current research interests
are model (mis)specification analysis in econometrics and its application in empir-
ical research, time series econometrics, and the econometric analysis of dynamic
stochastic general equilibrium models.
,
,Introduction to the Mathematical and Statistical Foundations
of Econometrics
This book is intended for use in a rigorous introductory Ph.D.-level course in econo-
metrics or in a field course in econometric theory. It covers the measure–theoretical
foundation of probability theory, the multivariate normal distribution with its ap-
plication 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 ap-
pendixes containing more advanced topics and/or difficult proofs. Moreover, there
are three appendixes with material that is supposed to be known. Appendix I con-
tains 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.
Herman J. Bierens is Professor of Economics at the Pennsylvania State Univer-
sity and part-time Professor of Econometrics at Tilburg University, The Nether-
lands. He is Associate Editor of the Journal of Econometrics and Econometric
Reviews, and has been an Associate Editor of Econometrica. Professor Bierens
has written two monographs, Robust Methods and Asymptotic Theory in Nonlin-
ear Econometrics and Topics in Advanced Econometrics (Cambridge University
Press 1994), as well as numerous journal articles. His current research interests
are model (mis)specification analysis in econometrics and its application in empir-
ical research, time series econometrics, and the econometric analysis of dynamic
stochastic general equilibrium models.
,
