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Matrix Differential Calculus with Applications in Statistics and Econometrics

Matrix Differential Calculus with Applications in Statistics and Econometrics PDF

Author: Jan R. Magnus and Heinz Neudecker

Publisher: Wiley


Publish Date: March 18, 2019

ISBN-10: 1119541204

Pages: 504

File Type: PDF

Language: English

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Book Preface

Preface to the first edition
There has been a long-felt need for a book that gives a self-contained and unified treatment of matrix differential calculus, specifically written for econometricians and statisticians. The present book is meant to satisfy this need.

It can serve as a textbook for advanced undergraduates and postgraduates in econometrics and as a reference book for practicing econometricians. Mathematical statisticians and psychometricians may also find something to their liking in the book.

When used as a textbook, it can provide a full-semester course. Reasonable proficiency in basic matrix theory is assumed, especially with the use of partitioned matrices. The basics of matrix algebra, as deemed necessary for a proper understanding of the main subject of the book, are summarized in

Part One, the first of the book’s six parts. The book also contains the essentials of multivariable calculus but geared to and often phrased in terms of differentials.

The sequence in which the chapters are being read is not of great consequence. It is fully conceivable that practitioners start with Part Three (Differentials: the practice) and, dependent on their predilections, carry on to Parts

Five or Six, which deal with applications. Those who want a full understanding of the underlying theory should read the whole book, although even then they could go through the necessary matrix algebra only when the specific need arises.

Matrix differential calculus as presented in this book is based on differentials, and this sets the book apart from other books in this area. The approach via differentials is, in our opinion, superior to any other existing approach.

Our principal idea is that differentials are more congenial to multivariable
functions as they crop up in econometrics, mathematical statistics, or psychometrics than derivatives, although from a theoretical point of view the two concepts are equivalent.

The book falls into six parts. Part One deals with matrix algebra. It lists,
and also often proves, items like the Schur, Jordan, and singular-value decompositions; concepts like the Hadamard and Kronecker products; the vecoperator; the commutation and duplication matrices; and the Moore-Penrose inverse. Results on bordered matrices (and their determinants) and (linearly restricted) quadratic forms are also presented here.

Part Two, which forms the theoretical heart of the book, is entirely devoted to a thorough treatment of the theory of differentials, and presents the essentials of calculus but geared to and phrased in terms of differentials.

First and second differentials are defined, ‘identification’ rules for Jacobian and Hessian matrices are given, and chain rules derived. A separate chapter on the theory of (constrained) optimization in terms of differentials concludes this part.

Part Three is the practical core of the book. It contains the rules for working with differentials, lists the differentials of important scalar, vector, and matrix functions (inter alia eigenvalues, eigenvectors, and the MoorePenrose inverse) and supplies ‘identification’ tables for Jacobian and Hessian matrices.

Part Four, treating inequalities, owes its existence to our feeling that econometricians should be conversant with inequalities, such as the Cauchy-Schwarz and Minkowski inequalities (and extensions thereof), and that they should also master a powerful result like Poincar´e’s separation theorem. This part is to some extent also the case history of a disappointment. When we started writing this book we had the ambition to derive all inequalities by means of matrix differential calculus. After all, every inequality can be rephrased as the solution of an optimization problem. This proved to be an illusion, due to the fact that the Hessian matrix in most cases is singular at the optimum point.

Part Five is entirely devoted to applications of matrix differential calculus to the linear regression model. There is an exhaustive treatment of estimation problems related to the fixed part of the model under various assumptions concerning ranks and (other) constraints. Moreover, it contains topics relating to the stochastic part of the model, viz. estimation of the error variance and prediction of the error term. There is also a small section on sensitivity analysis. An introductory chapter deals with the necessary statistical preliminaries.

Part Six deals with maximum likelihood estimation, which is of course an ideal source for demonstrating the power of the propagated techniques. In the first of three chapters, several models are analysed, inter alia the multivariate normal distribution, the errors-in-variables model, and the nonlinear regression model. There is a discussion on how to deal with symmetry and positive definiteness, and special attention is given to the information matrix. The second chapter in this part deals with simultaneous equations under normality conditions. It investigates both identification and estimation problems, subject to various (non)linear constraints on the parameters. This part also discusses full-information maximum likelihood (FIML) and limited-information maximum likelihood (LIML), with special attention to the derivation of asymptotic variance matrices. The final chapter addresses itself to various psychometric problems, inter alia principal components, multimode component analysis, factor analysis, and canonical correlation.

All chapters contain many exercises. These are frequently meant to be
complementary to the main text.

A large number of books and papers have been published on the theory and applications of matrix differential calculus. Without attempting to describe their relative virtues and particularities, the interested reader may wish to consult Dwyer and Macphail (1948), Bodewig (1959), Wilkinson (1965), Dwyer (1967), Neudecker (1967, 1969), Tracy and Dwyer (1969), Tracy and Singh (1972), McDonald and Swaminathan (1973), MacRae (1974), Balestra (1976), Bentler and Lee (1978), Henderson and Searle (1979), Wong and Wong (1979, 1980), Nel (1980), Rogers (1980), Wong (1980, 1985), Graham (1981), McCulloch (1982), Sch¨onemann (1985), Magnus and Neudecker (1985), Pollock (1985), Don (1986), and Kollo (1991). The papers by Henderson and Searle (1979) and Nel (1980), and Rogers’ (1980) book contain extensive

bibliographies. The two authors share the responsibility for Parts One, Three, Five, and Six, although any new results in Part One are due to Magnus. Parts Two and Four are due to Magnus, although Neudecker contributed some results to Part Four. Magnus is also responsible for the writing and organization of the final text.

We wish to thank our colleagues F. J. H. Don, R. D. H. Heijmans, D. S. G. Pollock, and R. Ramer for their critical remarks and contributions. The greatest obligation is owed to Sue Kirkbride at the London School of Economics who patiently and cheerfully typed and retyped the various versions of the book. Partial financial support was provided by the Netherlands Organization for the Advancement of Pure Research (Z. W. O.) and the Suntory Toyota International Centre for Economics and Related Disciplines at the London School of Economics.

London/Amsterdam Jan R. Magnus
April 1987 Heinz Neudecker

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