Mathematics for Econometrics

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Springer Science & Business Media, Sep 24, 2013 - Business & Economics - 419 pages
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This book deals with a number of mathematical topics that are of great importance in the study of classical econometrics. There is a lengthy chapter on matrix algebra, which takes the reader from the most elementary aspects to the partitioned inverses, characteristic roots and vectors, symmetric, and orthogonal and positive (semi) definite matrices. The book also covers pseudo-inverses, solutions to systems of linear equations, solutions of vector difference equations with constant coefficients and random forcing functions, matrix differentiation, and permutation matrices. Its novel features include an introduction to asymptotic expansions, and examples of applications to the general-linear model (regression) and the general linear structural econometric model (simultaneous equations).

 

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Contents

Chapter 1 Vectors and Vector Spaces
1
Chapter 2 Matrix Algebra
12
Chapter 3 Systems of Linear Equations
95
Chapter 4 Matrix Vectorization
119
Chapter 5 Vector and Matrix Differentiation
149
Chapter 6 DE Lag Operators GLSEM and Time Series
171
Chapter 7 Mathematical Underpinnings of Probability Theory
197
Chapter 8 Foundations of Probability
235
Chapter 9 LLN CLT and Ergodicity
285
Chapter 10 The General Linear Model
309
Chapter 11 Panel Data Models
335
Chapter 12 GLSEM and TS Models
351
Chapter 13 Asymptotic Expansions
393
Bibliography
411
Index
413
Copyright

Other editions - View all

Mathematics for Econometrics
Phoebus J. Dhrymes
No preview available - 2013
Mathematics for Econometrics
Phoebus J. Dhrymes
No preview available - 2013

Common terms and phrases

2SLS algebra autocovariance Borel characteristic roots characteristic vectors column vector complex numbers condition Consequently consider contains context converges covariance matrix defined denoted Dhrymes dimension discussion econometrics elements equation ergodic example exists finite g-inverse given GLSEM Hence identical implies indicator function integral inverse Lebesgue limiting distribution linear linearly independent matrix of order mean measurable function measurable space Moreover n-element nonnegative nonsingular nonsingular matrix notation null obeys obtain OLS estimator operation orthogonal matrix parameters partition permutation matrix polynomial positive definite positive semi-definite probability measure probability space Proof properties q.e.d. Corollary q.e.d. Proposition q.e.d. Remark random variables rank real numbers reduced form representation respectively restrictions result scalar solution square matrix subsets subspace suppose symmetric matrix term theorem Toeplitz matrix tr(A unique vec(A vec(X vector space zero

About the author (2013)

Professor Dhrymes is currently Professor of Economics at Columbia University. Earlier he taught at Harvard, University of Pennsylvania, University of California at Los Angeles, and Monash University in Australia.

He is a fellow of the Econometric Society and the American Statistical Association. He has been a managing editor and editor of the International Economic Review, and one of the founding editors of the Journal of Econometrics. Professor Dhrymes serves on the Editorial Advisory Boards of the Journal of Econometrics and Econometric Theory.

Bibliographic information

QR code for Mathematics for Econometrics
TitleMathematics for Econometrics
SpringerLink : Bücher
AuthorPhoebus J. Dhrymes
Edition4, illustrated
PublisherSpringer Science & Business Media, 2013
ISBN1461481457, 9781461481454
Length419 pages
Subjects ›  › 

Business & Economics / Econometrics
Business & Economics / General
Business & Economics / Statistics
Mathematics / Game Theory
Mathematics / Probability & Statistics / General
  
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