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
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