Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Classical Statistical Procedures -- The Score Vector, the Information Matrix, and the Cramer--Rao Lower Bound -- Maximum Likelihood Estimators and Test Procedures -- Nuisance Parameters -- Differentiation and Asymptotics -- Elements of Matrix Algebra -- Kronecker Products -- The Vec and the Devec Operators -- Generalized Vec and Devec Operators -- Triangular Matrices and Band Matrices -- Zero-One Matrices -- Selection Matrices and Permutation Matrices -- The Commutation Matrix and Generalized Vecs and Devecs of the Commutation Matrix -- Elimination Matrices L, L -- Duplication Matrices D, L -- Results Concerning Zero-One Matrices Associated with an n x n Matrix -- Shifting Matrices -- Matrix Calculus -- Some Simple Matrix Calculus Results -- Matrix Calculus and Zero-One Matrices -- The Chain Rule and the Product Rule for Matrix Calculus -- Rules for Vecs of Matrices -- Rules Developed from the Properties of K[subscript Gn], K[superscript [tau subscript n subscript Gn], and K[superscript [tau subscript n subscript nG] -- Rules for Scalar Functions of a Matrix -- Tables of Results -- Linear-Regression Models -- The Basic Linear-Regression Model -- The Linear-Regression Model with Autoregressive Disturbances -- Linear-Regression Model with Moving-Average Disturbances -- Probability Limits Associated with the Information Matrix for the Autoregressive Disturbances Model -- Probability Limits Associated with the Information Matrix for the Moving-Average Disturbances Model -- Seemingly Unrelated Regression Equations Models.

This 2002 book presents the reader with mathematical tools taken from matrix calculus and zero-one matrices and demonstrates how these tools greatly facilitate the application of classical statistical procedures to econometric models. The matrix calculus results are derived from a few basic rules that are generalizations of the rules of ordinary calculus. These results are summarized in a useful table. Well-known zero-one matrices, together with some newer ones, are defined, their mathematical roles explained, and their useful properties presented. The basic building blocks of classical statistics, namely the score vector, the information matrix, and the Cramer-Rao lower bound, are obtained for a sequence of linear econometric models of increasing statistical complexity. From these are obtained interactive interpretations of maximum likelihood estimators, linking them with efficient econometric estimators. Classical test statistics are also derived and compared for hypotheses of interest.