Description

Book Synopsis
This volume describes the algebra of matrices and shows how to apply them to today's problems in applied economics. The first section covers the essentials of matrices while the second section concentrates on major topics in applied economics such as regression, linear programming, and time series.

Trade Review
"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)

Table of Contents
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.

Matrix Algebra for Applied Economics

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    £135.85

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    RRP £150.95 – you save £15.10 (10%)

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    A Hardback by Shayle R. Searle, Lois Schertz Willett

      Trusted by thousands of customers. See 2,385+ Customer Reviews

      View other formats and editions of Matrix Algebra for Applied Economics by Shayle R. Searle

      Publisher: John Wiley & Sons Inc
      Publication Date: 14/09/2001
      ISBN13: 9780471322078, 978-0471322078
      ISBN10: 0471322075

      Description

      Book Synopsis
      This volume describes the algebra of matrices and shows how to apply them to today's problems in applied economics. The first section covers the essentials of matrices while the second section concentrates on major topics in applied economics such as regression, linear programming, and time series.

      Trade Review
      "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)

      Table of Contents
      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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