Matrix algebra for applied economics
著者
書誌事項
Matrix algebra for applied economics
(Wiley series in probability and mathematical statistics)
Wiley, c2001
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注記
"A Wiley-Interscience publication"
Includes bibliographical references (p. 389-392) and index
内容説明・目次
内容説明
Coverage of matrix algebra for economists and students ofeconomics
Matrix Algebra for Applied Economics explains the important tool ofmatrix algebra for students of economics and practicing economists.It includes examples that demonstrate the foundation operations ofmatrix algebra and illustrations of using the algebra for a varietyof economic problems.
The authors present the scope and basic definitions of matrices,their arithmetic and simple operations, and describe specialmatrices and their properties, including the analog of division.They provide in-depth coverage of necessary theory and deal withconcepts 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 simplifiedusing matrix algebra in linear equations, regression, linearmodels, 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 guidancefor advanced undergraduate students and also graduate students.Practicing economists who want to sharpen their skills will findthis book both practical and easy-to-read, no matter what theirapplied interests.
目次
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.
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