Peña / Tiao / Tsay A Course in Time Series Analysis

1. Auflage 2011
ISBN: 978-1-118-03122-3
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 496 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

New statistical methods and future directions of research in timeseries
A Course in Time Series Analysis demonstrates how to build timeseries models for univariate and multivariate time series data. Itbrings together material previously available only in theprofessional literature and presents a unified view of the mostadvanced procedures available for time series model building. Theauthors begin with basic concepts in univariate time series,providing an up-to-date presentation of ARIMA models, including theKalman filter, outlier analysis, automatic methods for buildingARIMA models, and signal extraction. They then move on to advancedtopics, focusing on heteroscedastic models, nonlinear time seriesmodels, Bayesian time series analysis, nonparametric time seriesanalysis, and neural networks. Multivariate time series coverageincludes presentations on vector ARMA models, cointegration, andmultivariate linear systems. Special features include:
* Contributions from eleven of the worldâ??s leading figuresin time series
* Shared balance between theory and application
* Exercise series sets
* Many real data examples
* Consistent style and clear, common notation in allcontributions
* 60 helpful graphs and tables
Requiring no previous knowledge of the subject, A Course in TimeSeries Analysis is an important reference and a highly usefulresource for researchers and practitioners in statistics,economics, business, engineering, and environmental analysis.
An Instructor's Manual presenting detailed solutions to all theproblems in the book is available upon request from the Wileyeditorial department.

Autoren/Hrsg.

Peña, Daniel

Tiao, George C.

Tsay, Ruey S.

Inhaltsverzeichnis

Introduction (D. Pe?a & G. Tiao).
BASIC CONCEPTS IN UNIVARIATE TIME SERIES.
Univariate Time Series: Autocorrelation, Linear Prediction,Spectrum, State Space Model (G. Wilson).
Univariate Autoregressive Moving Average Models (G. Tiao).
Model Fitting and Checking, and the Kalman Filter (G.Wilson).
Prediction and Model Selection (D. Pe?a).
Outliers, Influential Observations and Missing Data (D.Pe?a).
Automatic Modeling Methods for Univariate Series (V. Gomez & A.Maravall).
Seasonal Adjustment and Signal Extraction in Economic Time Series(V. Gomez & A. Maravall).
ADVANCED TOPICS IN UNIVARIATE TIME SERIES.
Heteroscedatic Models (R. Tsay).
Nonlinear Time Series Models (R. Tsay).
Bayesian Time Series Analysis (R. Tsay).
Nonparametric Time Series Analysis: Nonparametric Regression,Locally Weighted Regression, Autoregression and Quantile Regression(S. Heiler).
Neural Networks (K. Hornik & F. Leisch).
MULTIVARIATE TIME SERIES.
Vector ARMA Models (G. Tiao).
Cointegration in the VAR Model (S. Johansen).
Multivariate Linear Systems (M. Deistler).
References.
Index.

Über Autor(innen)

DANIEL PE?A, PhD, is Professor of Statistics, Universidad CarlosIII de Madrid.
GEORGE C. TIAO, PhD, is W. Allen Wallis Professor of Statistics andEconometrics, Graduate School of Business, University ofChicago.
RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Statisticsand Econometrics, Graduate School of Business, University ofChicago.