Synopsis
"Statistical Methods in Econometrics" is appropriate for beginning graduate courses in mathematical statistics and econometrics in which the foundations of probability and statistical theory are developed for application to econometric methodology. Because econometrics generally requires the study of several unknown parameters, emphasis is placed on estimation and hypothesis testing involving several parameters. Accordingly, special attention is paid to the multivariate normal and the distribution of quadratic forms. Lagrange multiplier tests are discussed in considerable detail, along with the traditional likelihood ration and Wald tests. Characteristic functions and their properties are fully exploited. Also asymptotic distribution theory, usually given only cursory treatment, is discussed in detail. The book assumes a working knowledge of advanced calculus (including integral calculus) basic probability and statistics, and linear algebra. Important properties from matrix algebra are summarized in the appendix. Numerous examples, exercises, and practice problems are also included. It covers both multivariate analysis and matrix algebra. It focuses on newer tests of hypotheses such as the Lagrange multiplier test. It discusses characteristic functions in depth. This material has evolved during 15 years of classroom instruction.
From the Back Cover
Statistical Methods in Econometrics is appropriate for beginning graduate courses in mathematical statistics and econometrics in which the foundations of probability and statistical theory are developed for application to econometric methodology. Because econometrics generally requires the study of several unknown parameters, emphasis is placed on estimation and hypothesis testing involving several parameters. Accordingly, special attention is paid to the multivariate normal and the distribution of quadratic forms. Lagrange multiplier tests are discussed in considerable detail, along with the traditional likelihood ration and Wald tests. Characteristic functions and their properties are fully exploited. Also asymptotic distribution theory, usually given only cursory treatment, is discussed in detail.
The book assumes a working knowledge of advanced calculus (including integral calculus) basic probability and statistics, and linear algebra. Important properties from matrix algebra are summarized in the appendix. Numerous examples, exercises, and practice problems are included.
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