A Course in Time Series AnalysisNew 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 uptodate 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:

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Contents
1  
PART I BASIC CONCEPTS IN UNIVARIATE TIME SERIES  23 
PART II ADVANCED TOPICS IN UNIVARIATE TIME SERIES  247 
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Common terms and phrases
additive outlier algorithm applied approach approximation ARCH models ARIMA model ARMA Assoc assume asymptotic autocorrelation function autoregressive model bandwidth Bayesian bilinear Biometrika chapter coefﬁcients cointegrating computed conditional consider correlation covariance criterion cycle deﬁned deﬁnition denote differencing distribution Econ effect equation error example Figure ﬁnal ﬁnite ﬁrst ﬁt ﬁtted ﬁtting ﬁxed forecast frequency GARCH Gibbs Gibbs sampling given Gomez identiﬁcation inﬂuence input Kalman ﬁlter kernel level shift likelihood function linear model linear regression Maravall matrix mean methods model identiﬁcation model selection multivariate neural networks nonlinear nonparametric nonstationary obtained outliers parameter estimates partial autocorrelation periodogram polynomial prediction procedure properties random regression residuals seasonal adjustment seasonal component Section Series Analysis series models speciﬁcation spectrum Stat statespace stationary stationary process Statistical stochastic structure threshold Tiao transfer function trend Tsay unit roots values variables variance vector volatility white noise zero