Детальная информация

Название: Mathematics for econometrics
Авторы: Dhrymes Phoebus J.
Выходные сведения: New York: Springer, 2013
Коллекция: Электронные книги зарубежных издательств; Общая коллекция
Тематика: Математика прикладная; Эконометрика
УДК: 51-7
ББК: 65в631
Тип документа: Другой
Тип файла: Другой
Язык: Английский
Права доступа: Доступ по паролю из сети Интернет (чтение, печать)
Ключ записи: RU\SPSTU\edoc\54991

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The fourth edition of this book continues to deal with a number of mathematical topics that are of great importance in the study of classical econometrics. The major expansion involves a more complete coverage of basic aspects of mathematics that continue to play an increasingly significant role in the literature of econometrics. Thus, the chapter on difference equations has been expanded to include enhanced treatment of lag operators (backward shift operators in the statistical literature) that are important not only in the context of the dynamic simultaneous equation GLSEM (general linear structural econometric model), but also time series analysis. New features in this edition include chapters on probability theory and the probabilistic basis 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). Also unique to this edition are two fairly extensive chapters on applications to the GLM (general linear model), GLSEM and time series analysis which treat issues relevant to their underlying theoretical bases, estimation and forecasting.

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