An Introduction to Econometric Theory

A guide to economics, statistics and finance that explores the mathematical foundations underling econometric methods

An Introduction to Econometric Theory offers a text to help in the mastery of the mathematics that underlie econometric methods and includes a detailed study of matrix algebra and distribution theory. Designed to be an accessible resource, the text explains in clear language why things are being done, and how previous material informs a current argument. The style is deliberately informal with numbered theorems and lemmas avoided. However, very few technical results are quoted without some form of explanation, demonstration or proof.

The author — a noted expert in the field — covers a wealth of topics including: simple regression, basic matrix algebra, the general linear model, distribution theory, the normal distribution, properties of least squares, unbiasedness and efficiency, eigenvalues, statistical inference in regression, t and F tests, the partitioned regression, specification analysis, random regressor theory, introduction to asymptotics and maximum likelihood. Each of the chapters is supplied with a collection of exercises, some of which are straightforward and others more challenging. This important text:

• Presents a guide for teaching econometric methods to undergraduate and graduate students ofeconomics, statistics or finance
• Offers proven classroom-tested material
• Contains sets of exercises that accompany each chapter
• Includes a companion website that hosts additional materials, solution manual and lecture slides

Written for undergraduates and graduate students of economics, statistics or finance, An Introduction to Econometric Theory is an essential beginner’s guide to the underpinnings of econometrics.

Keywords:

Elementary Data Analysis; Variables and Observations; 
Summary Statistics; Correlation; Regression; Computing the Regression Line; Multiple Regression; 
Matrix Representation; Systems of Equations; Matrix Algebra Basics; Rules of Matrix Algebra; Partitioned; Solving the Matrix Equation; Matrix Inversion; Determinant and Adjoint; Transposes and Products; Cramer’s Rule; Partitioning and Inversion; 
A Note on Computation; The Least Squares Solution; Linear Dependence and Rank; The General Linear Regression; Definite Matrices; Matrix Calculus; Probability Distributions; A Random Experiment; Properties of the Normal Distribution; Expected Values; Discrete Random Variables; Random Vectors; The Multivariate Normal Distribution; Other Continuous Distributions; Conditional Distributions; The Classical Regression Model; The Classical Assumptions