Springer - Read

More documents

Recommendations

Info

184 Chapter 6 Nonstationary and Seasonal Time Series Models ACF -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Figure 6-5 The sample ACF of the 0 10 20 30 40 series {Yt } in Figure 6.4. Lag (6.1.4), which has very similar coefficients but for which φ ∗ has all of its zeros outside the unit circle. In either case, however, if it is possible by differencing to generate a series with rapidly decaying sample ACF, then the differenced data set can be fitted by a low-order ARMA process whose autoregressive polynomial φ ∗ has zeros that are comfortably outside the unit circle. This means that the fitted parameters will be well away from the boundary of the allowable parameter set. This is desirable PACF -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Figure 6-6 The sample PACF of the 0 10 20 30 40 series {Yt } in Figure 6.4. Lag
Page 1 and 2:
Introduction to Time Series and For
Page 4:
This page intentionally left blank
Page 8:
Peter J. Brockwell Richard A. Davis
Page 12:
This page intentionally left blank
Page 16:
viii Preface Since the upgrade to I
Page 20:
x Contents 2.6. The Wold Decomposit
Page 24:
xii Contents 8.8.2. Observation-Dri
Page 28:
xiv Contents D.6. Model Properties
Page 32:
2 Chapter 1 Introduction Figure 1-1
Page 36:
4 Chapter 1 Introduction Figure 1-3
Page 40:
6 Chapter 1 Introduction 3.5% of th
Page 44:
8 Chapter 1 Introduction Example 1.
Page 48:
10 Chapter 1 Introduction where mt
Page 52:
12 Chapter 1 Introduction Figure 1-
Page 56:
14 Chapter 1 Introduction n/12 6,
Page 60:
16 Chapter 1 Introduction variable,
Page 64:
18 Chapter 1 Introduction at once t
Page 68:
20 Chapter 1 Introduction Example 1
Page 72:
22 Chapter 1 Introduction ACF -0.2
Page 76:
Page 80:
Page 84:
28 Chapter 1 Introduction can be co
Page 88:
30 Chapter 1 Introduction If the op
Page 92:
32 Chapter 1 Introduction The reest
Page 96:
Page 100:
36 Chapter 1 Introduction (a) The s
Page 104:
38 Chapter 1 Introduction It can al
Page 108:
40 Chapter 1 Introduction Problems
Page 112:
42 Chapter 1 Introduction 1.8. Let
Page 116:
44 Chapter 1 Introduction At this p
Page 120:
46 Chapter 2 Stationary Processes T
Page 124:
48 Chapter 2 Stationary Processes T
Page 128:
50 Chapter 2 Stationary Processes i
Page 132:
52 Chapter 2 Stationary Processes P
Page 136:
54 Chapter 2 Stationary Processes I
Page 140:
56 Chapter 2 Stationary Processes A
Page 144:
58 Chapter 2 Stationary Processes 2
Page 148:
60 Chapter 2 Stationary Processes w
Page 152:
62 Chapter 2 Stationary Processes A
Page 156:
Page 160:
Page 164:
Page 168:
70 Chapter 2 Stationary Processes
Page 172:
Page 176:
74 Chapter 2 Stationary Processes a
Page 180:
76 Chapter 2 Stationary Processes t
Page 184:
Page 188:
80 Chapter 2 Stationary Processes 2
Page 192:
Page 196:
84 Chapter 3 ARMA Models The proces
Page 200:
86 Chapter 3 ARMA Models where θ0
Page 204:
88 Chapter 3 ARMA Models The AR pol
Page 208:
90 Chapter 3 ARMA Models moving ave
Page 212:
92 Chapter 3 ARMA Models ACF -0.2 0
Page 216:
94 Chapter 3 ARMA Models can be fou
Page 220:
96 Chapter 3 ARMA Models Example 3.
Page 224:
98 Chapter 3 ARMA Models is entirel
Page 228:
100 Chapter 3 ARMA Models 3.3 Forec
Page 232:
102 Chapter 3 ARMA Models and rn
Page 236:
104 Chapter 3 ARMA Models and 2 E
Page 240:
106 Chapter 3 ARMA Models where the
Page 244:
108 Chapter 3 ARMA Models Problems
Page 248:
110 Chapter 3 ARMA Models 3.10. By
Page 252:
112 Chapter 4 Spectral Analysis 4.1
Page 256:
114 Chapter 4 Spectral Analysis (ii
Page 260:
116 Chapter 4 Spectral Analysis Not
Page 264:
118 Chapter 4 Spectral Analysis Exa
Page 268:
120 Chapter 4 Spectral Analysis Fig
Page 272:
Page 276:
124 Chapter 4 Spectral Analysis The
Page 280:
126 Chapter 4 Spectral Analysis fac
Page 284:
Page 288:
130 Chapter 4 Spectral Analysis Sin
Page 292:
Page 296:
134 Chapter 4 Spectral Analysis Pro
Page 300:
136 Chapter 4 Spectral Analysis 4.9
Page 304:
138 Chapter 5 Modeling and Forecast
Page 308:
Page 312:
Page 316:
Page 320:
Page 324:
Page 328:
Page 332:
Page 336:
Page 340:
Page 344: 158 Chapter 5 Modeling and Forecast
Page 384: This page intentionally left blank
Page 388: 180 Chapter 6 Nonstationary and Sea
Page 392: 182 Chapter 6 Nonstationary and Sea
Page 398: Figure 6-7 200 observations of the
Page 402: 6.2 Identification Techniques 187 6
Page 406: Figure 6-11 The Australian red wine
Page 410: 6.2 Identification Techniques 191 P
Page 414: 6.3 Unit Roots in Time Series Model
Page 426: 6.4 Forecasting ARIMA Models 199 of
Page 430: 6.4 Forecasting ARIMA Models 201 wh
Page 434: 6.5 Seasonal ARIMA Models 203 6.5 S
Page 438: Figure 6-15 The ACF of the model 6.
Page 442: 6.5 Seasonal ARIMA Models 207 ACF -
Page 446:
6.5 Seasonal ARIMA Models 209 and s
Page 450:
6.6 Regression with ARMA Errors 211
Page 454:
Page 458:
Page 462:
Page 466:
Problems Figure 6-19 The difference
Page 470:
Problems 221 6.9. Repeat Problem 6.
Page 474:
7 Multivariate Time Series 7.1 Exam
Page 478:
7.1 Examples 225 and a natural esti
Page 482:
Figure 7-3 The sample ACF ˆρ22 of
Page 486:
Figure 7-6 The sample correlations
Page 490:
7.2 Second-Order Properties of Mult
Page 494:
7.2 Second-Order Properties of Mult
Page 498:
7.3 Estimation of the Mean and Cova
Page 502:
7.3 Estimation of the Mean and Cova
Page 506:
Figure 7-7 The sample correlations
Page 510:
7.4 Multivariate ARMA Processes 241
Page 514:
7.4 Multivariate ARMA Processes 243
Page 518:
7.5 Best Linear Predictors of Secon
Page 522:
7.6 Modeling and Forecasting with M
Page 526:
Page 530:
Page 534:
Page 538:
7.7 Cointegration 255 Example 7.7.1
Page 542:
Problems 257 and derive the error c
Page 546:
8 State-Space Models 8.1 State-Spac
Page 550:
8.1 State-Space Representations 261
Page 554:
8.2 The Basic Structural Model 263
Page 558:
Figure 8-2 Sample ACF of the series
Page 562:
8.3 State-Space Representation of A
Page 566:
8.3 State-Space Representation of A
Page 570:
8.4 The Kalman Recursions 271 the v
Page 574:
8.4 The Kalman Recursions 273 Kalma
Page 578:
8.4 The Kalman Recursions 275 where
Page 582:
8.5 Estimation For State-Space Mode
Page 586:
Page 590:
Page 594:
Figure 8-5 The one-step predictors
Page 598:
8.6 State-Space Models with Missing
Page 602:
8.6 State-Space Models with Missing
Page 606:
8.7 The EM Algorithm 8.7 The EM Alg
Page 610:
8.7 The EM Algorithm 291 Example 8.
Page 614:
8.8 Generalized State-Space Models
Page 618:
Page 622:
Page 626:
Page 630:
Page 634:
Page 638:
Page 642:
Figure 8-8 Goals scored by England
Page 646:
Page 650:
Problems Problems 311 (see Problem
Page 654:
Problems 313 can be expressed as Y
Page 658:
Problems 315 and p(x2|y1) g(x2; y1
Page 662:
9 Forecasting Techniques 9.1 The AR
Page 666:
9.1 The ARAR Algorithm 319 and choo
Page 670:
9.1 The ARAR Algorithm 321 9.1.4 Ap
Page 674:
9.2 The Holt-Winters Algorithm 323
Page 678:
Figure 9-2 The data set DEATHS.TSM
Page 682:
Figure 9-3 The data set DEATHS.TSM
Page 686:
Figure 9-4 The first 132 values of
Page 690:
10Further Topics 10.1 Transfer Func
Page 694:
10.1 Transfer Function Models 333 1
Page 698:
10.1 Transfer Function Models 335 (
Page 702:
Figure 10-2 The sample correlation
Page 706:
10.1 Transfer Function Models 339 I
Page 710:
10.2 Intervention Analysis 341 form
Page 714:
10.3 Nonlinear Models 10.3 Nonlinea
Page 718:
Figure 10-5 A sequence generated by
Page 722:
10.3 Nonlinear Models 347 lation at
Page 726:
10.3 Nonlinear Models 349 is a part
Page 730:
Figure 10-7 A realization of the p
Page 734:
10.3 Nonlinear Models 353 process {
Page 738:
10.3 Nonlinear Models 355 Compariso
Page 742:
10.4 Continuous-Time Models 357 whe
Page 746:
10.4 Continuous-Time Models 359 i
Page 750:
10.5 Long-Memory Models 361 and Cov
Page 754:
10.5 Long-Memory Models 363 The spe
Page 758:
Problems Figure 10-13 The minimum a
Page 762:
Problems 367 c. For p ≥ 1, show t
Page 766:
A Random Variables and Probability
Page 770:
A.1 Distribution Functions and Expe
Page 774:
A.1 Distribution Functions and Expe
Page 778:
A.2 Random Vectors 375 The probabil
Page 782:
A.3 The Multivariate Normal Distrib
Page 786:
A.3 The Multivariate Normal Distrib
Page 790:
Problems Problems 381 A.1. Let X ha
Page 794:
B Statistical B.1 Least Squares Est
Page 798:
B.1 Least Squares Estimation 385 Th
Page 802:
B.2 Maximum Likelihood Estimation 3
Page 806:
B.4 Hypothesis Testing 389 Example
Page 810:
B.4 Hypothesis Testing 391 B.3.1, w
Page 814:
C Mean C.1 The Cauchy Criterion Squ
Page 818:
D An ITSM Tutorial D.1 Getting Star
Page 822:
D.2 Preparing Your Data for Modelin
Page 826:
D.2 Preparing Your Data for Modelin
Page 830:
Figure D-2 The logged AIRPASS.TSM s
Page 834:
D.3 Finding a Model for Your Data 4
Page 838:
Figure D-4 The sample ACF of the tr
Page 842:
Page 846:
Page 850:
D.4 Testing Your Model D.4 Testing
Page 854:
D.4 Testing Your Model 413 frequenc
Page 858:
D.5 Prediction D.5 Prediction 415 t
Page 862:
D.6 Model Properties 417 of differe
Page 866:
Figure D-12 The PACF of the model i
Page 870:
D.7 Multivariate Time Series 421 us
Page 874:
References Akaike, H. (1969), Fitti
Page 878:
References 425 Dempster, A.P., Lair
Page 882:
References 427 Mood, A.M., Graybill
Page 886:
A accidental deaths (DEATHS.TSM), 3
Page 890:
estimation of missing values in an
Page 894:
polynomial fitting, 28 population o
show all

Springer - Read

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?