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时间序列与预测（英文版 第2版）
作者：Peter J.Brockwell，Richard A.Davis 著
出版社：人民邮电出版社
出版时间：2009-03-01
ISBN：9787115196828
定价：¥69.00
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内容简介
《时间序列与预测（英文版）（第2版）》是时间序列领域的名著。特色在于注重实际应用。深浅适中，适用面广，示例和习题丰富，有微积分、线性代数和统计学基础知识即可阅读。书中全面介绍了经济、工程、自然科学和社会科学中所用的时间序列和预测方法，核心内容是平稳过程、ARMA模型和ARIMA模型、多元时间序列和状态空间模型、谱分析。书中配有时间序列软件包ITSM2000学生版，更加方便读者学习。
作者简介
Peter J．Brockwell 世界著名统计学家。ASA（美国统计协会）、IMS（数理统计学会）会士。科罗拉多州立大学统计系荣休教授。他是Journalof Time Series Analysis副主编，并Li Richard A．Davis合作开发了时间序列软件包ITSM2000。Richard A．Davis 世界著名统计学家。ASA（美国统计协会）、IMS（数理统计学会）会士。科罗拉多州立大学统计系教授，1997年至2005年担任该系的系主任。1 998年荣获计量经济学Koopmans奖。他是Stochastic Processes and Their Applications，Annals of Applied Probability等期刊编委，是Proceedings ofthe American Mathematics Society的统计学领域主编。
目录
1. Introduction
1.1. Examples of Time Series
1.2. Objectives of Time Series Analysis
1.3. Some Simple Time Series Models
1.3.1. Some Zero-Mean Models
1.3.2. Models with Trend and Seasonality
1.3.3. A General Approach to Time Series Modeling
1.4. Stationary Models and the Autocorrelation Function
1.4.1. The Sample Autocorrelation Function
1.4.2. A Model for the Lake Huron Data
1.5. Estimation and Elimination of Trend and Seasonal Components
1.5.1. Estimation and Elimination of Trend in the Absence of
Seasonality
1.5.2. Estimation and Elimination of Both Trend and
Seasonality
1.6. Testing the Estimated Noise Sequence
Problems
2. Stationary Processes
2.1. Basic Properties
2.2. Linear Processes
2.3. Introduction to ARMA Processes
2.4. Properties of the Sample Mean and Autocorrelation Function
2.4.1. Estimation of tz
2.4.2. Estimation of y(.) and p(.)
2.5. Forecasting Stationary Time Series
2.5.1. The Durbin-Levinson Algorithm
2.5.2. The Innovations Algorithm
2.5.3. Prediction of a Stationary Process in Terms of Infinitely
Many Past Values
2.6. The Wold Decomposition
Problems
3. ARMA Models
3.1. ARMA(p， q) Processes
3.2. The ACF and PACF of an ARMA(p， q) Process
3.2.1. Calculation of the ACVF
3.2.2. The Autocorrelation Function
3.2.3. The Partial Autocorrelation Function
3.2.4. Examples
3.3. Forecasting ARMA Processes
Problems
4. Spectral Analysis
4.1. Spectral Densities
4.2. The Periodogram
4.3. Time-Invariant Linear Filters
4.4. The Spectral Density of an ARMA Process
Problems
5. Modeling and Forecasting with ARMA Processes
5. I. Preliminary Estimation
5.1.1. Yule-Walker Estimation
5.1.2. Burgs Algorithm
5.1.3. The Innovations Algorithm
5.1.4. The Hannan-Rissanen Algorithm
5.2. Maximum Likelihood Estimation
5.3. Diagnostic Checking
5.3.1. The Graph of
5.3.2. The Sample ACF of the Residuals
5.3.3. Tests for Randomness of the Residuals
5.4. Forecasting
5.5. Order Selection
5.5.1. The FPE Criterion
5.5.2. The AICC Criterion
Problems
6. Nonstationary and Seasonal Time Series Models
6.1. ARIMA Models for Nonstationary Time Series
6.2. Identification Techniques
6.3. Unit Roots in Time Series Models
6.3.1. Unit Roots in Autoregressions
6.3.2. Unit Roots in Moving Averages
6.4. Forecasting ARIMA Models
6.4.1. The Forecast Function
6.5. Seasonal ARIMA Models
6.5.1. Forecasting SARIMA Processes
6.6. Regression with ARMA Errors
6.6.1. OLS and GLS Estimation
6.6.2. ML Estimation
Problems
7. Multivariate Time Series
7.1. Examples
7.2. Second-Order Properties of Multivariate Time Series
7.3. Estimation of the Mean and Covariance Function
7.3.1. Estimation of
7.3.2. Estimation of F(h)
7.3.3. Testing for Independence of Two Stationary Time Series
7.3.4. Bartletts Formula
7.4. Multivariate ARMA Processes
7.4.1. The Covariance Matrix Function of a Causal ARMA
Process
7.5. Best Linear Predictors of Second-Order Random Vectors
7.6. Modeling and Forecasting with Multivariate AR Processes
7.6.1. Estimation for Autoregressive Processes Using Whittles
Algorithm
7.6.2. Forecasting Multivariate Autoregressive Processes
7.7. Cointegration
Problems
8. State-Space Models
8.1. State-Space Representations
8.2. The Basic Structural Model
8.3. State-Space Representation of ARIMA Models
8.4. The Kalman Recursions
8.5. Estimation For State-Space Models
8.6. State-Space Models with Missing Observations
8.7. The EM Algorithm
8.8. Generalized State-Space Models
8.8.1. Parameter-Driven Models
8.8.2. Observation-Driven Models
Problems
9. Forecasting Techniques
9.1. The ARAR Algorithm
9.1.1. Memory Shortening
9.1.2. Fitting a Subset Autoregression
9.1.3. Forecasting
9.1.4. Application of the ARAR Algorithm
9.2. The Holt-Winters Algorithm
9.2.1. The Algorithm
9.2.2. Holt-Winters and ARIMA Forecasting
9.3. The Holt-Winters Seasonal Algorithm
9.3.1. The Algorithm
9.3.2. Holt-Winters Seasonal and ARIMA Forecasting
9.4. Choosing a Forecasting Algorithm
Problems
10. Further Topics
10.1. Transfer Function Models
10.1.1. Prediction Based on a Transfer Function Model
10.2. Intervention Analysis
10.3. Nonlinear Models
10.3.1. Deviations from Linearity
10.3.2. Chaotic Deterministic Sequences
10.3.3. Distinguishing Between White Noise and iid Sequences
10.3.4. Three Useful Classes of Nonlinear Models
10.3.5. Modeling Volatility
10.4. Continuous-Time Models
10.5. Long-Memory Models
Problems
A. Random Variables and Probability Distributions
A. 1. Distribution Functions and Expectation
A.2. Random Vectors
A.3. The Multivariate Normal Distribution
Problems
B Statistical Complements
C Mean Square Convergence
D An ITSM Tutorial
References
Index
1.1. Examples of Time Series
1.2. Objectives of Time Series Analysis
1.3. Some Simple Time Series Models
1.3.1. Some Zero-Mean Models
1.3.2. Models with Trend and Seasonality
1.3.3. A General Approach to Time Series Modeling
1.4. Stationary Models and the Autocorrelation Function
1.4.1. The Sample Autocorrelation Function
1.4.2. A Model for the Lake Huron Data
1.5. Estimation and Elimination of Trend and Seasonal Components
1.5.1. Estimation and Elimination of Trend in the Absence of
Seasonality
1.5.2. Estimation and Elimination of Both Trend and
Seasonality
1.6. Testing the Estimated Noise Sequence
Problems
2. Stationary Processes
2.1. Basic Properties
2.2. Linear Processes
2.3. Introduction to ARMA Processes
2.4. Properties of the Sample Mean and Autocorrelation Function
2.4.1. Estimation of tz
2.4.2. Estimation of y(.) and p(.)
2.5. Forecasting Stationary Time Series
2.5.1. The Durbin-Levinson Algorithm
2.5.2. The Innovations Algorithm
2.5.3. Prediction of a Stationary Process in Terms of Infinitely
Many Past Values
2.6. The Wold Decomposition
Problems
3. ARMA Models
3.1. ARMA(p， q) Processes
3.2. The ACF and PACF of an ARMA(p， q) Process
3.2.1. Calculation of the ACVF
3.2.2. The Autocorrelation Function
3.2.3. The Partial Autocorrelation Function
3.2.4. Examples
3.3. Forecasting ARMA Processes
Problems
4. Spectral Analysis
4.1. Spectral Densities
4.2. The Periodogram
4.3. Time-Invariant Linear Filters
4.4. The Spectral Density of an ARMA Process
Problems
5. Modeling and Forecasting with ARMA Processes
5. I. Preliminary Estimation
5.1.1. Yule-Walker Estimation
5.1.2. Burgs Algorithm
5.1.3. The Innovations Algorithm
5.1.4. The Hannan-Rissanen Algorithm
5.2. Maximum Likelihood Estimation
5.3. Diagnostic Checking
5.3.1. The Graph of
5.3.2. The Sample ACF of the Residuals
5.3.3. Tests for Randomness of the Residuals
5.4. Forecasting
5.5. Order Selection
5.5.1. The FPE Criterion
5.5.2. The AICC Criterion
Problems
6. Nonstationary and Seasonal Time Series Models
6.1. ARIMA Models for Nonstationary Time Series
6.2. Identification Techniques
6.3. Unit Roots in Time Series Models
6.3.1. Unit Roots in Autoregressions
6.3.2. Unit Roots in Moving Averages
6.4. Forecasting ARIMA Models
6.4.1. The Forecast Function
6.5. Seasonal ARIMA Models
6.5.1. Forecasting SARIMA Processes
6.6. Regression with ARMA Errors
6.6.1. OLS and GLS Estimation
6.6.2. ML Estimation
Problems
7. Multivariate Time Series
7.1. Examples
7.2. Second-Order Properties of Multivariate Time Series
7.3. Estimation of the Mean and Covariance Function
7.3.1. Estimation of
7.3.2. Estimation of F(h)
7.3.3. Testing for Independence of Two Stationary Time Series
7.3.4. Bartletts Formula
7.4. Multivariate ARMA Processes
7.4.1. The Covariance Matrix Function of a Causal ARMA
Process
7.5. Best Linear Predictors of Second-Order Random Vectors
7.6. Modeling and Forecasting with Multivariate AR Processes
7.6.1. Estimation for Autoregressive Processes Using Whittles
Algorithm
7.6.2. Forecasting Multivariate Autoregressive Processes
7.7. Cointegration
Problems
8. State-Space Models
8.1. State-Space Representations
8.2. The Basic Structural Model
8.3. State-Space Representation of ARIMA Models
8.4. The Kalman Recursions
8.5. Estimation For State-Space Models
8.6. State-Space Models with Missing Observations
8.7. The EM Algorithm
8.8. Generalized State-Space Models
8.8.1. Parameter-Driven Models
8.8.2. Observation-Driven Models
Problems
9. Forecasting Techniques
9.1. The ARAR Algorithm
9.1.1. Memory Shortening
9.1.2. Fitting a Subset Autoregression
9.1.3. Forecasting
9.1.4. Application of the ARAR Algorithm
9.2. The Holt-Winters Algorithm
9.2.1. The Algorithm
9.2.2. Holt-Winters and ARIMA Forecasting
9.3. The Holt-Winters Seasonal Algorithm
9.3.1. The Algorithm
9.3.2. Holt-Winters Seasonal and ARIMA Forecasting
9.4. Choosing a Forecasting Algorithm
Problems
10. Further Topics
10.1. Transfer Function Models
10.1.1. Prediction Based on a Transfer Function Model
10.2. Intervention Analysis
10.3. Nonlinear Models
10.3.1. Deviations from Linearity
10.3.2. Chaotic Deterministic Sequences
10.3.3. Distinguishing Between White Noise and iid Sequences
10.3.4. Three Useful Classes of Nonlinear Models
10.3.5. Modeling Volatility
10.4. Continuous-Time Models
10.5. Long-Memory Models
Problems
A. Random Variables and Probability Distributions
A. 1. Distribution Functions and Expectation
A.2. Random Vectors
A.3. The Multivariate Normal Distribution
Problems
B Statistical Complements
C Mean Square Convergence
D An ITSM Tutorial
References
Index
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