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 of economics, 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.

Table of Contents

Part I Fitting
Chapter 1 Elementary Data Analysis
Chapter 2 Matrix Representation
Chapter 3 Solving The Matrix Equation
Chapter 4 The Least Squares Solution

Part II Modelling
Chapter 5 Probability Distributions
Chapter 6 More On Distributions
Chapter 7 The Classical Regressionmodel
Chapter 8 The Gauss-Markov Theorem

Part III Testing
Chapter 9 Eigenvalues And Eigenvectors
Chapter 10 The Gaussian Regressionmodel
Chapter 11 Partitioning And Specification

Part IV Extensions
Chapter 12 Random Regressors
Chapter 13 Introduction To Asymptotics
Chapter 14 Asymptotic Estimation Theory

Part V Appendices
Appendix A The Binomial Coefficients
Appendix B The Exponential Function
Appendix C Essential Calculus
Appendix D The Generalized Inverse