[内容简介]
本书论述当代统计方法和非线性时间序列分析，着重阐述过去十年发展起来的非参数和半参数技术。主要内容包括相空间、频域及时域中的建模技术；为说明参数方法和非参数方法在时间序列数据分析中的一体性，本书给出某些参数化非线性模型的最新论述，如ARCH／GARCH模型和阈值模型；以及关于ARMA模型的一个简洁观点。本书始终使用实际应用中得到的数据，阐明如何借助非参数方法揭示高维数据的局部结构。本书还介绍了一些重要的技术工具。.
本书适合研究生，时间序列分析方面的实际工作者，该领域不同程度的研究人员。本书在统计界和诸如计量经济学、实证金融学、群体生物学及生态学之类的其他广泛领域都有其价值。阅读本书需要概率论和统计的基本知识。...
[目次]
Preface
1 Introduction
1.1 Examples of Times Series
1.2 Objectives of Time Series Analysis
1.3 Linear Time Series Models
1.4 What Is a Nonlinear Time Series?
1.5 Nonlinear Time Series Models
1.6 From Linear to Nonlinear Modes
1.7 Further Reading
1.8 Software Implementations
2 Characteristics of Time Series
2.1 Stationarity
2.2 Autocorrelation
2.3 Spectral Distributions
2.4 Periodogram
2.5 Long-Memory Processes
2.6 Mixing
2.7 Complements
2.8 Additional bibliographical Notes
3 ARMA Modeling and Forecasting
3.1 Models and Background
3.2 The Best Linear Prediction---Prewhitening
3.3 Maximum Likelihood Estimation
3.4 Order Determination
3.5 Diagnostic Checking
3.6 A Real Data Example---Analyzing German Egg Prices
3.7 Linear Forecasting
4 Parametric Nonlinear Time Series Modes
4.1 Threshold Models
4.2 ARCH and GARCh Models
4.3 Bilinear Models
4.4 Additional Bibliographical notes
5 Nonparametric Density Estimation
5.1 Introduction
5.2 Kernel Density Estimation
5.3 Windowing and Whitening
5.4 Bandwidth Selection
5.5 boundary Correction
5.6 Asymptotic Results
5.7 Complements---Proof of Theorem 5.3
5.8 Bibliographical Notes
6 Smoothing in Time Series
6.1 Introduction
6.2 Smoothing in the Time Domain
6.3 Smoothing in the State Domain
6.4 Spline Methods
6.5 Estimation of Conditional Densities
6.6 Complements
6.7 Bibliographical Notes
7 Spectral Density Estimation and Its Applications
7.1 Introduction
7.2 Tapering, Kernel Estimation, and Prewhitening
7.3 Automatic Estimation of Spectral Density
7.4 Tests for White Noise
7.5 Complements
7.6 bibliographical Notes
8 Nonparametric Models
8.1 Introduction
8.2 Multivatriate Local Polynomial Regression
8.3 Functional-Coefficient Autoregressive Model
8.4 Adaptive Functional-Coefficient Autoregressive Models
8.5 Additive Models
8.6 Other Nonparametric Models
8.7 Modeling Conditional Variance
8.8 Complements
8.9 Bibliographical Notes
9 Model Validation
9.1 Introduction
9.2 Generalized Likelihood Ration Tests
9.3 Tests on Spectral Densities
9.4 Autoregressive versus Nonparametric Models
9.5 Threshold Models versus Varying-Coefficient Models
9.6 Bibliographical Notes
10 Nonlinear Prediction
10.1 Features of Nonlinear Prediction
10.2 Point Prediction
10.3 Estimating Predictive Distributions
10.4 Interval Predictors and Predictive Sets
10.5 Complements
10.6 Additional Bibliographical Notes
References
Author index
Subject index