作者介绍

作者是加利福尼亚大学统计学院的教授。B.A. in Mathematics - 1958, Carleton CollegeM.S. in Statistics - 1960, Iowa State University;Ph.D. in Mathematical Statistics - 1965, George Washington University;

Elected Member - International Statistical Institute

Fellow - American Statistical Association

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 Aut oregression
6．10 Dynamic Linear Models with Switching
6．1．1 Stochastic Volatility
6．1．2 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 Joindy Stationary Series
7．4 Regression with Deterministic Inputs
7．5 Random Coefficient Regression
……
Appendix A Large Sample Theory
Appendix B Time Domain Theory
Appendix C SpectraIDomain Theory
Appendix R R Supplement
References
Index