1# yizhengchina
Preface to the Third Edition . vii
1 Characteristics of Time Series . 1
1.1 Introduction . 1
1.2 The Nature of Time Series Data . 3
1.3 Time Series Statistical Models . 11
1.4 Measures of Dependence: Autocorrelation and
Cross-Correlation . 17
1.5 Stationary Time Series . 22
1.6 Estimation of Correlation . 28
1.7 Vector-Valued and Multidimensional Series . 33
Problems . 39
2 Time Series Regression and Exploratory Data Analysis . 47
2.1 Introduction . 47
2.2 Classical Regression in the Time Series Context . 48
2.3 Exploratory Data Analysis . 57
2.4 Smoothing in the Time Series Context . 70
Problems . 78
3 ARIMA Models . 83
3.1 Introduction . 83
3.2 Autoregressive Moving Average Models . 84
3.3 Dierence Equations . 97
3.4 Autocorrelation and Partial Autocorrelation . 102
3.5 Forecasting . 108
3.6 Estimation . 121
3.7 Integrated Models for Nonstationary Data . 141
3.8 Building ARIMA Models . 144
3.9 Multiplicative Seasonal ARIMA Models . 154
Problems . 162
4 Spectral Analysis and Filtering . 173
4.1 Introduction . 173
4.2 Cyclical Behavior and Periodicity . 175
4.3 The Spectral Density . 180
4.4 Periodogram and Discrete Fourier Transform . 187
4.5 Nonparametric Spectral Estimation . 196
4.6 Parametric Spectral Estimation . 212
4.7 Multiple Series and Cross-Spectra . 216
4.8 Linear Filters . 221
4.9 Dynamic Fourier Analysis and Wavelets . 228
4.10 Lagged Regression Models . 242
4.11 Signal Extraction and Optimum Filtering . 247
4.12 Spectral Analysis of Multidimensional Series . 252
Problems . 255
5 Additional Time Domain Topics . 267
5.1 Introduction . 267
5.2 Long Memory ARMA and Fractional Dierencing . 267
5.3 Unit Root Testing . 277
5.4 GARCH Models . 280
5.5 Threshold Models . 289
5.6 Regression with Autocorrelated Errors . 293
5.7 Lagged Regression: Transfer Function Modeling . 296
5.8 Multivariate ARMAX Models . 301
Problems . 315

2# yizhengchina
6 State-Space Models . 319
6.1 Introduction . 319
6.2 Filtering, Smoothing, and Forecasting . 325
6.3 Maximum Likelihood Estimation . 335
6.4 Missing Data Modications . 344
6.5 Structural Models: Signal Extraction and Forecasting . 350
6.6 State-Space Models with Correlated Errors . 354
6.6.1 ARMAX Models . 355
6.6.2 Multivariate Regression with Autocorrelated Errors . 356
6.7 Bootstrapping State-Space Models . 359
6.8 Dynamic Linear Models with Switching . 365
6.9 Stochastic Volatility . 378
6.10 Nonlinear and Non-normal State-Space Models Using Monte
Carlo Methods . 387
Problems . 398
7 Statistical Methods in the Frequency Domain . 405
7.1 Introduction . 405
7.2 Spectral Matrices and Likelihood Functions . 409
7.3 Regression for Jointly Stationary Series . 410
7.4 Regression with Deterministic Inputs . 420
7.5 Random Coecient Regression . 429
7.6 Analysis of Designed Experiments . 434
7.7 Discrimination and Cluster Analysis . 450
7.8 Principal Components and Factor Analysis . 468
7.9 The Spectral Envelope . 485
Problems . 501
Appendix A: Large Sample Theory . 507
A.1 Convergence Modes . 507
A.2 Central Limit Theorems . 515
A.3 The Mean and Autocorrelation Functions . 518
Appendix B: Time Domain Theory . 527
B.1 Hilbert Spaces and the Projection Theorem . 527
B.2 Causal Conditions for ARMA Models . 531
B.3 Large Sample Distribution of the AR(p) Conditional Least
Squares Estimators . 533
B.4 The Wold Decomposition . 537
Appendix C: Spectral Domain Theory . 539
C.1 Spectral Representation Theorem . 539
C.2 Large Sample Distribution of the DFT and Smoothed
Periodogram . 543
C.3 The Complex Multivariate Normal Distribution . 554
Appendix R: R Supplement . 559
R.1 First Things First . 559
R.1.1 Included Data Sets . 560
R.1.2 Included Scripts . 562
R.2 Getting Started . 567
R.3 Time Series Primer . 571
References . 577
Index . 591

昨天的下了 正在看 今天的也不错 期待第三本啊 哈哈

第二版还没看呢，就来第三版了

谢谢拉谢谢啦