New Introduction to Multiple Time Series Analysis

Author: Helmut Lütkepohl

Publisher: Springer Science & Business Media

ISBN: 9783540262398

Category: Business & Economics

Page: 764

View: 2726

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This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.

Elements of Multivariate Time Series Analysis

Author: Gregory C. Reinsel

Publisher: Springer Science & Business Media

ISBN: 9780387406190

Category: Mathematics

Page: 358

View: 9773

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This text concentrates on the time-domain analysis of multivariate time series, and assumes a background in univariate time series analysis. It also includes exercise sets and multivariate time series data sets. The book should also be useful to researchers and graduate students in the areas of statistics, econometrics, business, and engineering.

Introduction to Statistical Time Series

Author: Wayne A. Fuller,J.K. Watson,Wayne Arthur Fuller

Publisher: John Wiley & Sons

ISBN: 9780471552390

Category: Mathematics

Page: 698

View: 6049

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The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models, nonlinearestimation, multivariate models, state space representations, andempirical model identification. New sections have also been addedon the Wold decomposition, partial autocorrelation, long memoryprocesses, and the Kalman filter. Major topics include: Moving average and autoregressive processes Introduction to Fourier analysis Spectral theory and filtering Large sample theory Estimation of the mean and autocorrelations Estimation of the spectrum Parameter estimation Regression, trend, and seasonality Unit root and explosive time series To accommodate a wide variety of readers, review material,especially on elementary results in Fourier analysis, large samplestatistics, and difference equations, has been included.

Time Series Analysis

Forecasting and Control

Author: George E. P. Box,Gwilym M. Jenkins,Gregory C. Reinsel

Publisher: Wiley

ISBN: 9780470272848

Category: Mathematics

Page: 784

View: 2282

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A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include: A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools New coverage of forecasting in the design of feedback and feedforward control schemes A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series A review of the maximum likelihood estimation for ARMA models with missing values Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis, Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.

The Econometric Analysis of Time Series

Author: Andrew C. Harvey

Publisher: MIT Press

ISBN: 9780262081894

Category: Business & Economics

Page: 387

View: 2036

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The Econometric Analysis of Time Series focuses on the statistical aspects of model building, with an emphasis on providing an understanding of the main ideas and concepts in econometrics rather than presenting a series of rigorous proofs.

Time Series Analysis

Author: James Douglas Hamilton

Publisher: Taylor & Francis US

ISBN: 9780691042893

Category: Business & Economics

Page: 799

View: 5313

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In addition, Hamilton presents traditional tools for analyzing dynamic systems, including linear representations, autocovariance, generating functions, spectral analysis, and the Kalman filter, illustrating their usefulness both for economic theory and for studying and interpreting real-world data.

Forecasting, Structural Time Series Models and the Kalman Filter

Author: Andrew C. Harvey

Publisher: Cambridge University Press

ISBN: 9780521405737

Category: Business & Economics

Page: 554

View: 8994

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A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.

The Econometric Analysis of Seasonal Time Series

Author: Eric Ghysels,Denise R. Osborn

Publisher: Cambridge University Press

ISBN: 9780521565882

Category: Business & Economics

Page: 228

View: 6831

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The treatment offers a thorough review of developments in econometric analysis of seasonal time series.

Likelihood-based Inference in Cointegrated Vector Autoregressive Models

Author: Søren Johansen

Publisher: Oxford University Press on Demand

ISBN: 9780198774501

Category: Business & Economics

Page: 267

View: 4825

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This monograph is concerned with the statistical analysis of multivariate systems of non-stationary time series of type I. It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model.

Mathematical Foundations of Time Series Analysis

A Concise Introduction

Author: Jan Beran

Publisher: Springer

ISBN: 3319743805

Category: Mathematics

Page: 307

View: 1802

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This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.