本書是Springer統計學教程系列之一，全面地講述了時頻域方法理論。在第1版的基礎上增加了不少新的內容，大量的實例結合統計軟體的應用，使本書的實用性更強。本書包括分類時間序列分析、譜包絡、多元譜方法、長記憶序列、非線性模型、縱向數據分析、重抽樣技巧、Garch模型、隨機波動性模型、小波和Monte Carlo Markov鏈積分方法最近發展比較迅速的話題。在本版中將這些材料劃分為更小的章節，講述更加詳細，金融時間序列講述的範圍也更加廣闊，包括GARCH和隨機波動模型。
作者介紹
(美)羅伯特·沙姆韋//戴維·斯托弗|責編:陳亮
目錄
Preface to the Fourth Edition Preface to the Third Edition 1 Characteristics of Time Series 1.1 The Nature of Time Series Data 1.2 Time Series Statistical Models 1.3 Measures of Dependence 1.4 Stationary Time Series 1.5 Estimation of Correlation 1.6 Vector-Valued and Multidimensional Series Problems 2 Time Series Regression and Exploratory Data Analysis 2.1 Classical Regression in the Time Series Context 2.2 Exploratory Data Analysis 2.3 Smoothing in the Time Series Context Problems 3 ARIMA Models 3.1 Autoregressive Moving Average Models 3.2 Difference Equations 3.3 Autocorrelation and Partial Autocorrelation 3.4 Forecasting 3.5 Estimation 3.6 Integrated Models for Nonstationary Data 3.7 Building ARIMA Models 3.8 Regression with Autocorrelated Errors 3.9 Multiplicative Seasonal ARIMA Models Problems 4 Spectral Analysis and Filtering 4.1 Cyclical Behavior and Periodicity 4.2 The Spectral Density 4.3 Periodogram and Discrete Fourier Transform 4.4 Nonparametric Spectral Estimation 4.5 Parametric Spectral Estimation 4.6 Multiple Series and Cross-Spectra 4.7 Linear Filters 4.8 Lagged Regression Models 4.9 Signal Extraction and Optimum Filtering 4.10 Spectral Analysis of Multidimensional Series Problems 5 Additional Time Domain Topics 5.1 Long Memory ARMA and Fractional Differencing 5.2 Unit Root Testing 5.3 GARCH Models 5.4 Threshold Models 5.5 Lagged Regression and Transfer Function Modeling 5.6 Multivariate ARMAX Models Problems 6 State Space Models 6.1 Linear Gaussian Model 6.2 Filtering, Smoothing, and Forecasting 6.3 Maximum Likelihood Estimation
6.4 Missing Data Modifications 6.5 Structural Models: Signal Extraction and Forecasting 6.6 State-Space Models with Correlated Errors 6.6.1 ARMAX Models 6.6.2 Multivariate Regression with Autocorrelated Errors 6.7 Bootstrapping State Space Models 6.8 Smoothing Splines and the Kalman Smoother 6.9 Hidden Markov Models and Switching Autoregression 6.10 Dynamic Linear Models with Switching 6.11 Stochastic Volatility 6.12 Bayesian Analysis of State Space Models Problems 7 Statistical Methods in the Frequency Domain 7.1 Introduction 7.2 Spectral Matrices and Likelihood Functions 7.3 Regression for Jointly Stationary Series 7.4 Regression with Deterministic Inputs 7.5 Random Coefficient Regression 7.6 Analysis of Designed Experiments 7.7 Discriminant and Cluster Analysis 7.8 Principal Components and Factor Analysis 7.9 The Spectral Envelope Problems Appendix A Large Sample Theory A.1 Convergence Modes A.2 Central Limit Theorems A.3 The Mean and Autocorrelation Functions Appendix B Time Domain Theory B.1 Hilbert Spaces and the Projection Theorem B.2 Causal Conditions for ARMA Models B.3 Large Sample Distribution of the AR Conditional Least Squares Estimators B.4 The Wold Decomposition Appendix C Spectral Domain Theory C.1 Spectral Representation Theorems C.2 Large Sample Distribution of the Smoothed Periodogram C.3 The Complex Multivariate Normal Distribution C.4 Integration C.4.1 Riemann-Stieltjes Integration C.4.2 Stochastic Integration C.5 Spectral Analysis as Principal Component Analysis C.6 Parametric Spectral Estimation Appendix R R Supplement R.1 First Things First R.2 astsa R.3 Getting Started R.4 Time Series Primer R.4.1 Graphics References Index
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