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"Damodar N. Gujarati's classic text is praised for being logically organized and accessible, providing students with an overview of the basics of econometric theory from ordinal logistic regression to time series. The material is introduced in a clear, concise manner, with extensive examples, and a large number of questions and problems at the end of each chapter to test mastery. The Fifth Edition includes new chapters on time series econometrics and panel data econometrics, and new examples throughout. Appendices to the book provide reviews of the statistics needed to understand the econometric theory and practice discussed in the text. Resources for instructors and students are provided on an accompanying website for the book."

Random set theory is a fascinating branch of mathematics that amalgamates techniques from topology, convex geometry, and probability theory. Social scientists routinely conduct empirical work with data and modelling assumptions that reveal a set to which the parameter of interest belongs, but not its exact value. Random set theory provides a coherent mathematical framework to conduct identification analysis and statistical inference in this setting and has become a fundamental tool in econometrics and finance. This is the first book dedicated to the use of the theory in econometrics, written to be accessible for readers without a background in pure mathematics. Molchanov and Molinari define the basics of the theory and illustrate the mathematical concepts by their application in the analysis of econometric models. The book includes sets of exercises to accompany each chapter as well as examples to help readers apply the theory effectively

Front Cover; Foundations of Econometrics; Copyright Page; Preface; Table of Contents; Chapter 1. Matrix theory; 1. Matrix operations; 2. Euclidean space and linear transformations; 3. Matricial representations of linear transformations; 4. Projection transformations; 5. Determinants; 6. Orthogonal transformations and symmetric matrices; 7. Generalized inverse; 8. Derivatives of functions of matrices; 9. References; Chapter 2. Multivariate statistical analysis: Distribution and point estimation theory; 1. Multivariate probability distributions; 2. Multivariate normal distribution.