時間序列分析及其套用第2版)

《時間序列分析及其套用（第2版）》是2009年5月世界圖書出版公司出版的圖書，作者是[美]羅伯特沙姆韋 。

基本介紹

• 書名：時間序列分析及其套用（第2版）
• 作者：[美]羅伯特沙姆韋
• 出版社：世界圖書出版公司
• 出版時間：2009年5月
• ISBN：9787510004384 

內容提要

The goals of this book are to develop aappreciatiofor the richness andversatility of modertime series analysis as a tool for analyzing data， and stillmaintaia *mitment to theoretical integrity， as exemplified by the seminalworks of Brillinger （1981） and Hanna（1970） and the texts by Brockwell andDavis （1991） and Fuller （1995）. The advent of more powerful *puting， es-pecially ithe last three years， has provided both real data and new softwarethat catake one considerably beyond the fitting of simple time domaimod-els， such as have beeelegantly described ithe landmark work of Box andJenkins （see Box et al.， 1994）. This book is designed to be useful as a textfor courses itime series oseveral different levels and as a reference workfor practitioners facing the analysis of time-correlated data ithe physical，biological， and social sciences.

目錄

序言

1 Characteristics of Time Series

1.1 Introduction

1.2 The Nature of Time Series Data

1.3 Time Series Statistical Models

1.4 Measures of Dependence: Autocorrelatioand Cross-Correlation

1.5 Stationary Time Series

1.6 Estimatioof Correlation

1.7 Vector-Valued and Multidimensional Series

Problems

2 Time Series Regressioand Exploratory Data Analysis

2.1 Introduction

2.2 Classical Regressioithe Time Series Context

2.3 Exploratory Data Analysis

2.4 Smoothing ithe Time Series Context

Problems

3 ARIMA Models

3.1 Introduction

3.2 Autoregressive Moving Average Models

3.3 Difference Equations

3.4 Autocorrelatioand Partial AutocorrelatioFunctions

3.5 Forecasting

3.6 Estimation

3.7 Integrated Models for Nonstationary Data

3.8 Building ARIMA Models

3.9 Multiplicative Seasonal ARIMA Models

Problems

4 Spectral Analysis and Filtering

4.1 Introduction

4.2 Cyclical Behavior and Periodicity

4.3 The Spectral Density

4.4 Periodogram and Discrete Fourier Transform

4.5 Nonparametric Spectral Estimation

4.6 Multiple Series and Cross-Spectra

4.7 Linear Filters

4.8 Parametric Spectral Estimation

4.9 Dynamic Fourier Analysis and Wavelets

4.10 Lagged RegressioModels

4.11 Signal Extractioand Optimum Filtering

4.12 Spectral Analysis of Multidimensional Series

Problems

5 Additional Time DomaiTopics

5.1 Introduction

5.2 Long Memory ARMA and Fractional Differencing

5.3 GARCH Models

5.4 Threshold Models

5.5 Regressiowith Autocorrelated Errors

5.6 Lagged Regression: Transfer FunctioModeling

5.7 Multivariate ARMAX Models

Problems

6 State-Space Models

6.1 Introduction

6.2 Filtering, Smoothing, and Forecasting

6.3 Ma*mum Likelihood Estimation

6.4 Missing Data Modifications

6.5 Structural Models: Signal Extractioand Forecasting

6.6 ARMAX Models iState-Space Form

6.7 Bootstrapping State-Space Models

6.8 Dynamic Linear Models with Switching

6.9 Nonlinear and Non-normal State-Space Models Using Monte Carlo Methods

6.10 Stochastic Volatility

6.11 State-Space and ARMAX Models for Longitudinal Data Analysis

Problems

7 Statistical Methods ithe Frequency Domain

7.1 Introduction

7.2 Spectral Matrices and Likelihood Functions

7.3 Regressiofor Jointly Stationary Series

7.4 Regressiowith Deterministic Inputs

7.5 Random Coefficient Regression

7.6 Analysis of Designed Experiments

7.7 Discriminatioand 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 Meaand AutocorrelatioFunctions

Appendix B: Time DomaiTheory

B.1 Hilbert Spaces and the ProjectioTheorem

B.2 Causal Conditions for ARMA Models

B.3 Large Sample Distributioof the AR(p) Conditional Least Squares Estimators

B.4 The Wold De*position

Appendix C: Spectral DomaiTheory

C.1 Spectral RepresentatioTheorem

C.2 Large Sample Distributioof the DFT and Smoothed Periodogram

C.3 The Complex Multivariate Normal Distribution

References

Index