Книга Short-Memory Linear Processes and Econometric Applications Kairat T. Mynbaev

Short-Memory Linear Processes and Econometric Applications

Автор: Kairat T. Mynbaev
Език: Английски език
Корици: С твърди корици
Издател: John Wiley & Sons Inc
Наличност: Външен склад в ограничено количество
Изпращаме след 11-15 дни
160.79 314.48 лв
This book serves as a comprehensive source of asymptotic results for econometric models with determi...

Информация за книгата

Автор
Език
Английски език
Корици
Книга - С твърди корици
Издадена
2011
страници
452
EAN
9780470924198
ISBN
0470924195
Enbook ID
04886811
Издател
Теглоt
780
Размери
168 x 245 x 27

Пълно описание

This book serves as a comprehensive source of asymptotic results for econometric models with deterministic exogenous regressors. Such regressors include linear (more generally, piece-wise polynomial) trends, seasonally oscillating functions, and slowly varying functions including logarithmic trends, as well as some specifications of spatial matrices in the theory of spatial models. The book begins with central limit theorems (CLTs) for weighted sums of short memory linear processes. This part contains the analysis of certain operators in Lp spaces and their employment in the derivation of CLTs. The applications of CLTs are to the asymptotic distribution of various estimators for several econometric models. Among the models discussed are static linear models with slowly varying regressors, spatial models, time series autoregressions, and two nonlinear models (binary logit model and nonlinear model whose linearization contains slowly varying regressors). The estimation procedures include ordinary and nonlinear least squares, maximum likelihood, and method of moments. Additional topical coverage includes an introduction to operators, probabilities, and linear models; Lp-approximable sequences of vectors; convergence of linear and quadratic forms; regressions with slowly varying regressors; spatial models; convergence; nonlinear models; and tools for vector autoregressions.

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