Time series analysis: with applications in R
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York, NY
Springer
[2008]
|
| Edition: | Second edition |
| Series: | Springer texts in statistics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xiii, 491 Seiten Diagramme |
| ISBN: | 9780387759586 0387759581 9781441926135 |
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MARC
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| 245 | 1 | 0 | |a Time series analysis |b with applications in R |c Jonathan D. Cryer ; Kung-Sik Chan |
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| adam_text | Contents
CHAPTER 1 INTRODUCTION 1
1.1 Examples of Time Series 1
1.2 A Model-Building Strategy 8
1.3 Time Series Plots in History 8
1.4 An Overview of the Book 9
Exercises 10
Chapter 2 Fundamental Concepts n
2.1 Time Series and Stochastic Processes 11
2.2 Means, Variances, and Covariances 11
2.3 Stationarity 16
2.4 Summary 19
Exercises 19
Appendix A: Expectation, Variance, Covariance, and Correlation. 24
Chapter 3 Trends 27
3.1 Deterministic Versus Stochastic Trends 27
3.2 Estimation of a Constant Mean 28
3.3 Regression Methods 30
3.4 Reliability and Efficiency of Regression Estimates 36
3.5 Interpreting Regression Output 40
3.6 Residual Analysis 42
3.7 Summary 50
Exercises 50
Chapter 4 Models for Stationary Time Series 55
4.1 General Linear Processes 55
4.2 Moving Average Processes 57
4.3 Autoregressive Processes 66
4.4 The Mixed Autoregressive Moving Average Model 77
4.5 Invertibility 79
4.6 Summary 80
Exercises 81
Appendix B: The Stationarity Region for an AR(2) Process 84
Appendix C: The Autocorrelation Function for ARMA(p,q) 85
ix
x Contents
Chapter 5 Models for Nonstationary Time Series .87
5.1 Stationarity Through Differencing 88
5.2 ARIMA Models 92
5.3 Constant Terms in ARIMA Models 97
5.4 Other Transformations 98
5.5 Summary 102
Exercises 103
Appendix D: The Backshift Operator 106
Chapter 6 Model Specification 109
6.1 Properties of the Sample Autocorrelation Function 109
6.2 The Partial and Extended Autocorrelation Functions 112
6.3 Specification of Some Simulated Time Series 117
6.4 Nonstationarity 125
6.5 Other Specification Methods 130
6.6 Specification of Some Actual Time Series 133
6.7 Summary 141
Exercises 141
Chapter 7 Parameter Estimation 149
7.1 The Method of Moments 149
7.2 Least Squares Estimation 154
7.3 Maximum Likelihood and Unconditional Least Squares .. .158
7.4 Properties of the Estimates 160
7.5 Illustrations of Parameter Estimation 163
7.6 Bootstrapping ARIMA Models 167
7.7 Summary 170
Exercises 170
Chapter 8 Model Diagnostics 175
8.1 Residual Analysis 175
8.2 Overfitting and Parameter Redundancy 185
8.3 Summary 188
Exercises 188
Contents xi
CHAPTER 9 FORECASTING 191
9.1 Minimum Mean Square Error Forecasting 191
9.2 Deterministic Trends 191
9.3 ARIMA Forecasting 193
9.4 Prediction Limits 203
9.5 Forecasting Illustrations 204
9.6 Updating ARIMA Forecasts 207
9.7 Forecast Weights and Exponentially Weighted
Moving Averages 207
9.8 Forecasting Transformed Series 209
9.9 Summary of Forecasting with Certain ARIMA Models.... 211
9.10 Summary 213
Exercises 213
Appendix E: Conditional Expectation 218
Appendix F: Minimum Mean Square Error Prediction 218
Appendix G: The Truncated Linear Process 221
Appendix H: State Space Models 222
Chapter 10 Seasonal Models 227
10.1 Seasonal ARIMA Models 228
10.2 Multiplicative Seasonal ARMA Models 230
10.3 Nonstationary Seasonal ARIMA Models 233
10.4 Model Specification, Fitting, and Checking 234
10.5 Forecasting Seasonal Models 241
10.6 Summary 246
Exercises 246
Chapter 11 Time Series Regression Models 249
11.1 Intervention Analysis 249
11.2 Outliers 257
11.3 Spurious Correlation 260
11.4 Prewhitening and Stochastic Regression 265
11.5 Summary 273
Exercises 274
xjj Contents
Chapter 12 Time Series Models of
HETEROSCEDASTICITY 277
12.1 Some Common Features of Financial Time Series 278
12.2 The ARCH(1) Model 285
12.3 GARCH Models 289
12.4 Maximum Likelihood Estimation 298
12.5 Model Diagnostics 301
12.6 Conditions for the Nonnegativity of the
Conditional Variances 307
12.7 Some Extensions of the GARCH Model 310
12.8 Another Example: The Daily USD/HKD Exchange Rates . .311
12.9 Summary 315
Exercises 316
Appendix I: Formulas for the Generalized Portmanteau Tests .. .318
Chapter 13 Introduction to Spectral Analysis. .. .319
13.1 Introduction 319
13.2 The Periodogram 322
13.3 The Spectral Representation and Spectral Distribution... .327
13.4 The Spectral Density 330
13.5 Spectral Densities for ARMA Processes 332
13.6 Sampling Properties of the Sample Spectral Density 340
13.7 Summary 346
Exercises 346
Appendix J: Orthogonality of Cosine and Sine Sequences 349
Chapter 14 Estimating the Spectrum 351
14.1 Smoothing the Spectral Density 351
14.2 Bias and Variance 354
14.3 Bandwidth 355
14.4 Confidence Intervals for the Spectrum 356
14.5 Leakage and Tapering 358
14.6 Autoregressive Spectrum Estimation 363
14.7 Examples with Simulated Data 364
14.8 Examples with Actual Data 370
14.9 Other Methods of Spectral Estimation 376
14.10Summary 378
Exercises 378
Appendix K: Tapering and the Dirichlet Kernel 381
Contents xiii
Chapter 15 Threshold Models 383
15.1 Graphically Exploring Nonlinearity 384
15.2 Tests for Nonlinearity 390
15.3 Polynomial Models Are Generally Explosive 393
15.4 First-Order Threshold Autoregressive Models 395
15.5 Threshold Models 399
15.6 Testing for Threshold Nonlinearity 400
15.7 Estimation of a TAR Model 402
15.8 Model Diagnostics 411
15.9 Prediction 415
15.10Summary 420
Exercises 420
Appendix L: The Generalized Portmanteau Test for TAR 421
Appendix: An Introduction to R 423
Introduction 423
Chapter 1 R Commands 429
Chapter 2 R Commands 433
Chapter 3 R Commands 433
Chapter 4 R Commands 438
Chapter 5 R Commands 439
Chapter 6 R Commands 441
Chapter 7 R Commands 442
Chapter 8 R Commands 446
Chapter 9 R Commands 447
Chapter 10 R Commands 450
Chapter 11 R Commands 451
Chapter 12 R Commands 457
Chapter 13 R Commands 460
Chapter 14 R Commands 461
Chapter 15 R Commands 462
New or Enhanced Functions in the TSA Library 468
Dataset Information 471
BlBLIOGRAPHY 477
Index 487
Springer
Texts in Statistics
Time Series Analysis: With Applications in R, Second Edition, presents an accessible approach to
understanding time series models and their applications. Although the emphasis is on time
domain ARIMA models and their analysis, the new edition devotes two chapters to the fre¬
quency domain and three to time series regression models, models for heteroscedasticty, and
threshold models. All of the ideas and methods are illustrated with both real and simulated
data sets.
A unique feature of this edition is its integration with the
R
computing environment. The
tables and graphical displays are accompanied by the
R
commands used to produce them. An
extensive
R
package, TSA, which contains many new or revised
R
functions and all of the data
used in the book, accompanies the written text. Script files of
R
commands for each chapter are
available for download. There is also an extensive appendix in the book that leads the reader
through the use of
R
commands and the new
R
package to carry out the analyses.
Jonathan D. Cryer is Professor Emeritus, University of Iowa, in the Department of Statistics
and Actuarial Science. He is a Fellow of the American Statistical Association and received a
Collegiate Teaching Award from the University of Iowa College of Liberal Arts and Sciences. He
is the author of Statistics for Business: Data Analysis and Modeling, Second Edition, (with
Robert B. Miller), the Minitab Handbook, Fifth Edition, (with Barbara Ryan and Brian Joiner),
the Electronic Companion to Statistics (with George Cobb), Electronic Companion to Business
Statistics (with George Cobt)) and numerous research papers.
Kung-Sik Chan is Professor, University of Iowa, in the Department of Statistics and Actuarial
Science. He is a Fellow of the American Statistical Association and the Institute of the
Mathematical Statistics, and an Elected Member of the International Statistical Institute. He
received a Faculty Scholar Award from the University of Iowa in
1996.
He is the author of
Chaos: A Statistical Perspective (with Howell
Tong)
and numerous research papers.
|
| adam_txt |
Contents
CHAPTER 1 INTRODUCTION 1
1.1 Examples of Time Series 1
1.2 A Model-Building Strategy 8
1.3 Time Series Plots in History 8
1.4 An Overview of the Book 9
Exercises 10
Chapter 2 Fundamental Concepts n
2.1 Time Series and Stochastic Processes 11
2.2 Means, Variances, and Covariances 11
2.3 Stationarity 16
2.4 Summary 19
Exercises 19
Appendix A: Expectation, Variance, Covariance, and Correlation. 24
Chapter 3 Trends 27
3.1 Deterministic Versus Stochastic Trends 27
3.2 Estimation of a Constant Mean 28
3.3 Regression Methods 30
3.4 Reliability and Efficiency of Regression Estimates 36
3.5 Interpreting Regression Output 40
3.6 Residual Analysis 42
3.7 Summary 50
Exercises 50
Chapter 4 Models for Stationary Time Series 55
4.1 General Linear Processes 55
4.2 Moving Average Processes 57
4.3 Autoregressive Processes 66
4.4 The Mixed Autoregressive Moving Average Model 77
4.5 Invertibility 79
4.6 Summary 80
Exercises 81
Appendix B: The Stationarity Region for an AR(2) Process 84
Appendix C: The Autocorrelation Function for ARMA(p,q) 85
ix
x Contents
Chapter 5 Models for Nonstationary Time Series .87
5.1 Stationarity Through Differencing 88
5.2 ARIMA Models 92
5.3 Constant Terms in ARIMA Models 97
5.4 Other Transformations 98
5.5 Summary 102
Exercises 103
Appendix D: The Backshift Operator 106
Chapter 6 Model Specification 109
6.1 Properties of the Sample Autocorrelation Function 109
6.2 The Partial and Extended Autocorrelation Functions 112
6.3 Specification of Some Simulated Time Series 117
6.4 Nonstationarity 125
6.5 Other Specification Methods 130
6.6 Specification of Some Actual Time Series 133
6.7 Summary 141
Exercises 141
Chapter 7 Parameter Estimation 149
7.1 The Method of Moments 149
7.2 Least Squares Estimation 154
7.3 Maximum Likelihood and Unconditional Least Squares . .158
7.4 Properties of the Estimates 160
7.5 Illustrations of Parameter Estimation 163
7.6 Bootstrapping ARIMA Models 167
7.7 Summary 170
Exercises 170
Chapter 8 Model Diagnostics 175
8.1 Residual Analysis 175
8.2 Overfitting and Parameter Redundancy 185
8.3 Summary 188
Exercises 188
Contents xi
CHAPTER 9 FORECASTING 191
9.1 Minimum Mean Square Error Forecasting 191
9.2 Deterministic Trends 191
9.3 ARIMA Forecasting 193
9.4 Prediction Limits 203
9.5 Forecasting Illustrations 204
9.6 Updating ARIMA Forecasts 207
9.7 Forecast Weights and Exponentially Weighted
Moving Averages 207
9.8 Forecasting Transformed Series 209
9.9 Summary of Forecasting with Certain ARIMA Models. 211
9.10 Summary 213
Exercises 213
Appendix E: Conditional Expectation 218
Appendix F: Minimum Mean Square Error Prediction 218
Appendix G: The Truncated Linear Process 221
Appendix H: State Space Models 222
Chapter 10 Seasonal Models 227
10.1 Seasonal ARIMA Models 228
10.2 Multiplicative Seasonal ARMA Models 230
10.3 Nonstationary Seasonal ARIMA Models 233
10.4 Model Specification, Fitting, and Checking 234
10.5 Forecasting Seasonal Models 241
10.6 Summary 246
Exercises 246
Chapter 11 Time Series Regression Models 249
11.1 Intervention Analysis 249
11.2 Outliers 257
11.3 Spurious Correlation 260
11.4 Prewhitening and Stochastic Regression 265
11.5 Summary 273
Exercises 274
xjj Contents
Chapter 12 Time Series Models of
HETEROSCEDASTICITY 277
12.1 Some Common Features of Financial Time Series 278
12.2 The ARCH(1) Model 285
12.3 GARCH Models 289
12.4 Maximum Likelihood Estimation 298
12.5 Model Diagnostics 301
12.6 Conditions for the Nonnegativity of the
Conditional Variances 307
12.7 Some Extensions of the GARCH Model 310
12.8 Another Example: The Daily USD/HKD Exchange Rates . .311
12.9 Summary 315
Exercises 316
Appendix I: Formulas for the Generalized Portmanteau Tests . .318
Chapter 13 Introduction to Spectral Analysis. . .319
13.1 Introduction 319
13.2 The Periodogram 322
13.3 The Spectral Representation and Spectral Distribution. .327
13.4 The Spectral Density 330
13.5 Spectral Densities for ARMA Processes 332
13.6 Sampling Properties of the Sample Spectral Density 340
13.7 Summary 346
Exercises 346
Appendix J: Orthogonality of Cosine and Sine Sequences 349
Chapter 14 Estimating the Spectrum 351
14.1 Smoothing the Spectral Density 351
14.2 Bias and Variance 354
14.3 Bandwidth 355
14.4 Confidence Intervals for the Spectrum 356
14.5 Leakage and Tapering 358
14.6 Autoregressive Spectrum Estimation 363
14.7 Examples with Simulated Data 364
14.8 Examples with Actual Data 370
14.9 Other Methods of Spectral Estimation 376
14.10Summary 378
Exercises 378
Appendix K: Tapering and the Dirichlet Kernel 381
Contents xiii
Chapter 15 Threshold Models 383
15.1 Graphically Exploring Nonlinearity 384
15.2 Tests for Nonlinearity 390
15.3 Polynomial Models Are Generally Explosive 393
15.4 First-Order Threshold Autoregressive Models 395
15.5 Threshold Models 399
15.6 Testing for Threshold Nonlinearity 400
15.7 Estimation of a TAR Model 402
15.8 Model Diagnostics 411
15.9 Prediction 415
15.10Summary 420
Exercises 420
Appendix L: The Generalized Portmanteau Test for TAR 421
Appendix: An Introduction to R 423
Introduction 423
Chapter 1 R Commands 429
Chapter 2 R Commands 433
Chapter 3 R Commands 433
Chapter 4 R Commands 438
Chapter 5 R Commands 439
Chapter 6 R Commands 441
Chapter 7 R Commands 442
Chapter 8 R Commands 446
Chapter 9 R Commands 447
Chapter 10 R Commands 450
Chapter 11 R Commands 451
Chapter 12 R Commands 457
Chapter 13 R Commands 460
Chapter 14 R Commands 461
Chapter 15 R Commands 462
New or Enhanced Functions in the TSA Library 468
Dataset Information 471
BlBLIOGRAPHY 477
Index 487
Springer
Texts in Statistics
Time Series Analysis: With Applications in R, Second Edition, presents an accessible approach to
understanding time series models and their applications. Although the emphasis is on time
domain ARIMA models and their analysis, the new edition devotes two chapters to the fre¬
quency domain and three to time series regression models, models for heteroscedasticty, and
threshold models. All of the ideas and methods are illustrated with both real and simulated
data sets.
A unique feature of this edition is its integration with the
R
computing environment. The
tables and graphical displays are accompanied by the
R
commands used to produce them. An
extensive
R
package, TSA, which contains many new or revised
R
functions and all of the data
used in the book, accompanies the written text. Script files of
R
commands for each chapter are
available for download. There is also an extensive appendix in the book that leads the reader
through the use of
R
commands and the new
R
package to carry out the analyses.
Jonathan D. Cryer is Professor Emeritus, University of Iowa, in the Department of Statistics
and Actuarial Science. He is a Fellow of the American Statistical Association and received a
Collegiate Teaching Award from the University of Iowa College of Liberal Arts and Sciences. He
is the author of Statistics for Business: Data Analysis and Modeling, Second Edition, (with
Robert B. Miller), the Minitab Handbook, Fifth Edition, (with Barbara Ryan and Brian Joiner),
the Electronic Companion to Statistics (with George Cobb), Electronic Companion to Business
Statistics (with George Cobt)) and numerous research papers.
Kung-Sik Chan is Professor, University of Iowa, in the Department of Statistics and Actuarial
Science. He is a Fellow of the American Statistical Association and the Institute of the
Mathematical Statistics, and an Elected Member of the International Statistical Institute. He
received a Faculty Scholar Award from the University of Iowa in
1996.
He is the author of
Chaos: A Statistical Perspective (with Howell
Tong)
and numerous research papers. |
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| ctrlnum | (OCoLC)191760003 (DE-599)DNB986966789 |
| dewey-full | 519.55/0285 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.55/0285 |
| dewey-search | 519.55/0285 |
| dewey-sort | 3519.55 3285 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | Second edition |
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| id | DE-604.BV023205411 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T20:09:49Z |
| indexdate | 2024-07-09T21:13:01Z |
| institution | BVB |
| isbn | 9780387759586 0387759581 9781441926135 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016391577 |
| oclc_num | 191760003 |
| open_access_boolean | |
| owner | DE-945 DE-473 DE-BY-UBG DE-384 DE-M347 DE-19 DE-BY-UBM DE-824 DE-706 DE-703 DE-11 DE-521 DE-83 |
| owner_facet | DE-945 DE-473 DE-BY-UBG DE-384 DE-M347 DE-19 DE-BY-UBM DE-824 DE-706 DE-703 DE-11 DE-521 DE-83 |
| physical | xiii, 491 Seiten Diagramme |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in statistics |
| spelling | Cryer, Jonathan D. Verfasser (DE-588)12999684X aut Time series analysis with applications in R Jonathan D. Cryer ; Kung-Sik Chan Second edition New York, NY Springer [2008] © 2008 xiii, 491 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Springer texts in statistics R (Langage de programmation) R (computerprogramma) gtt Série chronologique - Informatique Tijdreeksen gtt Datenverarbeitung R (Computer program language) Time-series analysis Data processing R Programm (DE-588)4705956-4 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s R Programm (DE-588)4705956-4 s DE-604 Chan, Kung-sik Verfasser (DE-588)1056134372 aut Erscheint auch als Online-Ausgabe 978-0-387-75959-3 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016391577&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016391577&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Cryer, Jonathan D. Chan, Kung-sik Time series analysis with applications in R R (Langage de programmation) R (computerprogramma) gtt Série chronologique - Informatique Tijdreeksen gtt Datenverarbeitung R (Computer program language) Time-series analysis Data processing R Programm (DE-588)4705956-4 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4067486-1 |
| title | Time series analysis with applications in R |
| title_auth | Time series analysis with applications in R |
| title_exact_search | Time series analysis with applications in R |
| title_exact_search_txtP | Time series analysis with applications in R |
| title_full | Time series analysis with applications in R Jonathan D. Cryer ; Kung-Sik Chan |
| title_fullStr | Time series analysis with applications in R Jonathan D. Cryer ; Kung-Sik Chan |
| title_full_unstemmed | Time series analysis with applications in R Jonathan D. Cryer ; Kung-Sik Chan |
| title_short | Time series analysis |
| title_sort | time series analysis with applications in r |
| title_sub | with applications in R |
| topic | R (Langage de programmation) R (computerprogramma) gtt Série chronologique - Informatique Tijdreeksen gtt Datenverarbeitung R (Computer program language) Time-series analysis Data processing R Programm (DE-588)4705956-4 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | R (Langage de programmation) R (computerprogramma) Série chronologique - Informatique Tijdreeksen Datenverarbeitung R (Computer program language) Time-series analysis Data processing R Programm Zeitreihenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016391577&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016391577&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cryerjonathand timeseriesanalysiswithapplicationsinr AT chankungsik timeseriesanalysiswithapplicationsinr |