TAILIEUCHUNG - Introduction to the Mathematical and Statistical Foundations of Econometrics

This book is intended for a rigorous introductory . level course in econometrics, or for use in a field course in econometric theory. It is based on lecture notes that I have developed during the period 1997-2003 for the first semester econometrics course “Introduction to Econometrics” in the core of the . program in economics at the Pennsylvania State University. Initially these lecture notes were written as a companion to Gallant’s (1997) textbook, but have been developed gradually into an alternative textbook. Therefore, the topics that are covered in this book encompass those in Gallant’s book, but in much more depth. Moreover, in order to make. | 1 Introduction to the Mathematical and Statistical Foundations of Econometrics Herman J. Bierens Pennsylvania State University USA and Tilburg University the Netherlands 2 Contents Preface Chapter 1 Probability and Measure . The Texas lotto Introduction Binomial numbers Sample space Algebras and sigma-algebras of events Probability measure . Quality control Sampling without replacement Quality control in practice Sampling with replacement Limits of the hypergeometric and binomial probabilities . Why do we need sigma-algebras of events . Properties of algebras and sigma-algebras General properties Borel sets . Properties of probability measures . The uniform probability measure Introduction Outer measure . Lebesgue measure and Lebesgue integral Lebesgue measure Lebesgue integral . Random variables and their distributions 3 Random variables and vectors Distribution functions . Density functions . Conditional probability Bayes rule and independence Conditional probability Bayes rule Independence . Exercises Appendices . Common structure of the proofs of Theorems 6 and 10 . Extension of an outer measure to a probability measure Chapter 2 Borel Measurability Integration and Mathematical Expectations . Introduction . Borel measurability . Integrals of Borel measurable functions with respect to a probability measure . General measurability and integrals of random variables with respect to probability measures . Mathematical expectation . Some useful inequalities involving mathematical expectations Chebishev s inequality Holder s inequality Liapounov s inequality Minkowski s inequality Jensen s inequality . Expectations of products of independent random variables . Moment generating functions and characteristic functions Moment generating .

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