Matrix Algebra for Applied Economics

Inbunden, Engelska, 2001

Del 349 i serien Wiley Series in Probability and Statistics

2 022 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

Produktinformation

Utgivningsdatum:2001-09-14
Mått:162 x 246 x 25 mm
Vikt:714 g
Format:Inbunden
Språk:Engelska
Serie:Wiley Series in Probability and Statistics
Antal sidor:432
Förlag:John Wiley & Sons Inc
ISBN:9780471322078

Utforska kategorier

Nationalekonomi inom Ekonomi och Ledarskap
Algebra inom Naturvetenskap och teknik

Mer om författaren

SHAYLE R. SEARLE, PhD, is Professor Emeritus of Biometry at CornellUniversity. He is the author of Linear Models, Linear Models forUnbalanced Data, Matrix Algebra Useful for Statistics, and also(with C. E. McCulloch) Generalized, Linear, and Mixed Models, allfrom Wiley. LOIS SCHERTZ WILLETT, PhD, is Professor of Food and ResourceEconomics at the University of Florida. She is the author ofnumerous scientific publications on economic markets and priceanalysis and has won several teaching awards for her instruction ineconomics, econometrics, and other quantitative analysis courses.

Recensioner i media

"Suitable for a graduate level course on matrix for statisticsstudents. The book is written in an applied style and students willlike it." (Journal of Statistical Computation andSimulation, March 2006) "...well suited to its purpose...content is beautifully laidout..." (Mathematics Today, June 2004) "...designed for students of economics and for practicingeconomists..." (Quarterly of Applied Mathematics, Vol. LX,No. 1, March 2002) "Textbook suitable for a one-semester course introduces matrixalgebra and its application to economic problems." (Journal ofEconomic Literature, Vol. 40, No. 1, March 2002) "A textbook...explaining to students of economics how matrixalgebra is used in the profession...No prior mathematics is assumedbeyond high school algebra..." (Reference & Research BookNews, May 2002) "...practising economists who want to gain more mathematicalskills will also find the book at the right level..."(Zentralblatt Math, Vol. 982, No. 07, 2002) "...an excellent introductory text..." (Journal of theAmerican Statistical Association, December 1, 2002)

Innehållsförteckning

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.