Matrix Algebra For Applied Economics

Coverage of matrix algebra for economists and students of economics

Matrix Algebra for Applied Economics explains the important tool of matrix algebra for students of economics and practicing economists. It includes examples that demonstrate the foundation operations of matrix algebra and illustrations of using the algebra for a variety of economic problems.

The authors present the scope and basic definitions of matrices, their arithmetic and simple operations, and describe special matrices and their properties, including the analog of division. They provide in-depth coverage of necessary theory and deal with concepts and operations for using matrices in real-life situations. They discuss linear dependence and independence, as well as rank, canonical forms, generalized inverses, eigenroots, and vectors. Topics of prime interest to economists are shown to be simplified using matrix algebra in linear equations, regression, linear models, linear programming, and Markov chains.

Highlights include:
* Numerous examples of real-world applications
* Challenging exercises throughout the book
* Mathematics understandable to readers of all backgrounds
* Extensive up-to-date reference material


Matrix Algebra for Applied Economics provides excellent guidance for advanced undergraduate students and also graduate students. Practicing economists who want to sharpen their skills will find this book both practical and easy-to-read, no matter what their applied interests.

SHAYLE R. SEARLE, PhD, is Professor Emeritus of Biometry at Cornell University. He is the author of Linear Models, Linear Models for Unbalanced Data, Matrix Algebra Useful for Statistics, and also (with C. E. McCulloch) Generalized, Linear, and Mixed Models, all from Wiley.

LOIS SCHERTZ WILLETT, PhD, is Professor of Food and Resource Economics at the University of Florida. She is the author of numerous scientific publications on economic markets and price analysis and has won several teaching awards for her instruction in economics, econometrics, and other quantitative analysis courses.

List of Chapters.

Preface.

BASICS.

Introduction.

Basic Matrix Operations.

Special Matrices.

Determinants.

Inverse Matrices.

NECESSARY THEORY.

Linearly (IN)Dependent Vectors.

Rank.

Canonical Forms.

Generalized Inverses.

Solving Linear Equations.

Eigenroots and Eigenvectors.

Miscellanea.

WORKING WITH MATRICES.

Applying Linear Equations.

Regression Analysis.

Linear Statistical Models.

Linear Programming.

Markov Chain Models.

References.

Index.