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New statistical methods and future directions of research in time series

A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:

  Contributions from eleven of the worldâ ™s leading figures in time series

  Shared balance between theory and application

  Exercise series sets

  Many real data examples

  Consistent style and clear, common notation in all contributions

  60 helpful graphs and tables

Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

Table of Contents

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.

About the Author

 

DANIEL PE?A, PhD, is Professor of Statistics, Universidad Carlos III de Madrid.

GEORGE C. TIAO, PhD, is W. Allen Wallis Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.

RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.