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Praise for the Fourth Edition
"The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control."
- Mathematical Reviews
Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.
Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include:
* A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series
* An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models
* Numerous examples drawn from finance, economics, engineering, and other related fields
* The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting
* Updates to literature references throughout and new end-of-chapter exercises
* Streamlined chapter introductions and revisions that update and enhance the exposition
Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.
"The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control."
- Mathematical Reviews
Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.
Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include:
* A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series
* An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models
* Numerous examples drawn from finance, economics, engineering, and other related fields
* The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting
* Updates to literature references throughout and new end-of-chapter exercises
* Streamlined chapter introductions and revisions that update and enhance the exposition
Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.
Praise for the Fourth Edition
"The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control."
- Mathematical Reviews
Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.
Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include:
* A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series
* An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models
* Numerous examples drawn from finance, economics, engineering, and other related fields
* The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting
* Updates to literature references throughout and new end-of-chapter exercises
* Streamlined chapter introductions and revisions that update and enhance the exposition
Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.
"The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control."
- Mathematical Reviews
Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.
Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include:
* A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series
* An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models
* Numerous examples drawn from finance, economics, engineering, and other related fields
* The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting
* Updates to literature references throughout and new end-of-chapter exercises
* Streamlined chapter introductions and revisions that update and enhance the exposition
Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.
Inhaltsverzeichnis
PREFACE TO THE FIFTH EDITION xix
PREFACE TO THE FOURTH EDITION xxiii
PREFACE TO THE THIRD EDITION xxv
1 Introduction 1
1.1 Five Important Practical Problems 2
1.2 Stochastic and Deterministic Dynamic Mathematical Models 6
1.3 Basic Ideas in Model Building 14
Appendix A1.1 Use of the R Software 17
Exercises 18
PART ONE STOCHASTIC MODELS AND THEIR FORECASTING 19
2 Autocorrelation Function and Spectrum of Stationary Processes 21
2.1 Autocorrelation Properties of Stationary Models 21
2.2 Spectral Properties of Stationary Models 34
Appendix A2.1 Link Between the Sample Spectrum and Autocovariance
Function Estimate 43
Exercises 44
3 Linear Stationary Models 47
3.1 General Linear Process 47
3.2 Autoregressive Processes 54
3.3 Moving Average Processes 68
3.4 Mixed Autoregressive--Moving Average Processes 75
Appendix A3.1 Autocovariances Autocovariance Generating Function and Stationarity Conditions for a General Linear Process 82
Appendix A3.2 Recursive Method for Calculating Estimates of Autoregressive Parameters 84
Exercises 86
4 Linear Nonstationary Models 88
4.1 Autoregressive Integrated Moving Average Processes 88
4.2 Three Explicit Forms for the ARIMA Model 97
4.3 Integrated Moving Average Processes 106
Appendix A4.1 Linear Difference Equations 116
Appendix A4.2 IMA(0 1 1) Process with Deterministic Drift 121
Appendix A4.3 ARIMA Processes with Added Noise 122
Exercises 126
5 Forecasting 129
5.1 Minimum Mean Square Error Forecasts and Their Properties 129
5.2 Calculating Forecasts and Probability Limits 135
5.3 Forecast Function and Forecast Weights 139
5.4 Examples of Forecast Functions and Their Updating 144
5.5 Use of State-Space Model Formulation for Exact Forecasting 155
5.6 Summary 162
Appendix A5.1 Correlation Between Forecast Errors 164
Appendix A5.2 Forecast Weights for any Lead Time 166
Appendix A5.3 Forecasting in Terms of the General Integrated Form 168
Exercises 174
PART TWO STOCHASTIC MODEL BUILDING 177
6 Model Identification 179
6.1 Objectives of Identification 179
6.2 Identification Techniques 180
6.3 Initial Estimates for the Parameters 194
6.4 Model Multiplicity 202
Appendix A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process 206
Exercises 207
7 Parameter Estimation 209
7.1 Study of the Likelihood and Sum-of-Squares Functions 209
7.2 Nonlinear Estimation 226
7.3 Some Estimation Results for Specific Models 236
7.4 Likelihood Function Based on the State-Space Model 242
7.5 Estimation Using Bayes' Theorem 245
Appendix A7.1 Review of Normal Distribution Theory 251
Appendix A7.2 Review of Linear Least-Squares Theory 256
Appendix A7.3 Exact Likelihood Function for Moving Average and Mixed Processes 259
Appendix A7.4 Exact Likelihood Function for an Autoregressive Process 266
Appendix A7.5 Asymptotic Distribution of Estimators for Autoregressive Models 274
Appendix A7.6 Examples of the Effect of Parameter Estimation Errors on Variances of Forecast Errors and Probability Limits for Forecasts 277
Appendix A7.7 Special Note on Estimation ofMoving Average Parameters 280
Exercises 280
8 Model Diagnostic Checking 284
8.1 Checking the Stochasti
PREFACE TO THE FOURTH EDITION xxiii
PREFACE TO THE THIRD EDITION xxv
1 Introduction 1
1.1 Five Important Practical Problems 2
1.2 Stochastic and Deterministic Dynamic Mathematical Models 6
1.3 Basic Ideas in Model Building 14
Appendix A1.1 Use of the R Software 17
Exercises 18
PART ONE STOCHASTIC MODELS AND THEIR FORECASTING 19
2 Autocorrelation Function and Spectrum of Stationary Processes 21
2.1 Autocorrelation Properties of Stationary Models 21
2.2 Spectral Properties of Stationary Models 34
Appendix A2.1 Link Between the Sample Spectrum and Autocovariance
Function Estimate 43
Exercises 44
3 Linear Stationary Models 47
3.1 General Linear Process 47
3.2 Autoregressive Processes 54
3.3 Moving Average Processes 68
3.4 Mixed Autoregressive--Moving Average Processes 75
Appendix A3.1 Autocovariances Autocovariance Generating Function and Stationarity Conditions for a General Linear Process 82
Appendix A3.2 Recursive Method for Calculating Estimates of Autoregressive Parameters 84
Exercises 86
4 Linear Nonstationary Models 88
4.1 Autoregressive Integrated Moving Average Processes 88
4.2 Three Explicit Forms for the ARIMA Model 97
4.3 Integrated Moving Average Processes 106
Appendix A4.1 Linear Difference Equations 116
Appendix A4.2 IMA(0 1 1) Process with Deterministic Drift 121
Appendix A4.3 ARIMA Processes with Added Noise 122
Exercises 126
5 Forecasting 129
5.1 Minimum Mean Square Error Forecasts and Their Properties 129
5.2 Calculating Forecasts and Probability Limits 135
5.3 Forecast Function and Forecast Weights 139
5.4 Examples of Forecast Functions and Their Updating 144
5.5 Use of State-Space Model Formulation for Exact Forecasting 155
5.6 Summary 162
Appendix A5.1 Correlation Between Forecast Errors 164
Appendix A5.2 Forecast Weights for any Lead Time 166
Appendix A5.3 Forecasting in Terms of the General Integrated Form 168
Exercises 174
PART TWO STOCHASTIC MODEL BUILDING 177
6 Model Identification 179
6.1 Objectives of Identification 179
6.2 Identification Techniques 180
6.3 Initial Estimates for the Parameters 194
6.4 Model Multiplicity 202
Appendix A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process 206
Exercises 207
7 Parameter Estimation 209
7.1 Study of the Likelihood and Sum-of-Squares Functions 209
7.2 Nonlinear Estimation 226
7.3 Some Estimation Results for Specific Models 236
7.4 Likelihood Function Based on the State-Space Model 242
7.5 Estimation Using Bayes' Theorem 245
Appendix A7.1 Review of Normal Distribution Theory 251
Appendix A7.2 Review of Linear Least-Squares Theory 256
Appendix A7.3 Exact Likelihood Function for Moving Average and Mixed Processes 259
Appendix A7.4 Exact Likelihood Function for an Autoregressive Process 266
Appendix A7.5 Asymptotic Distribution of Estimators for Autoregressive Models 274
Appendix A7.6 Examples of the Effect of Parameter Estimation Errors on Variances of Forecast Errors and Probability Limits for Forecasts 277
Appendix A7.7 Special Note on Estimation ofMoving Average Parameters 280
Exercises 280
8 Model Diagnostic Checking 284
8.1 Checking the Stochasti
Details
| Erscheinungsjahr: | 2015 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 720 |
| Inhalt: | 720 S. |
| ISBN-13: | 9781118675021 |
| ISBN-10: | 1118675029 |
| Sprache: | Englisch |
| Herstellernummer: | 1W118675020 |
| Autor: |
Box, George E. P.
Jenkins, Gwilym M. Reinsel, Gregory C. Ljung, Greta M. |
| Auflage: | 5. Aufl. |
| Hersteller: |
Wiley
Wiley & Sons |
| Maße: | 258 x 184 x 39 mm |
| Von/Mit: | George E. P. Box (u. a.) |
| Erscheinungsdatum: | 07.08.2015 |
| Gewicht: | 1,514 kg |
Inhaltsverzeichnis
PREFACE TO THE FIFTH EDITION xix
PREFACE TO THE FOURTH EDITION xxiii
PREFACE TO THE THIRD EDITION xxv
1 Introduction 1
1.1 Five Important Practical Problems 2
1.2 Stochastic and Deterministic Dynamic Mathematical Models 6
1.3 Basic Ideas in Model Building 14
Appendix A1.1 Use of the R Software 17
Exercises 18
PART ONE STOCHASTIC MODELS AND THEIR FORECASTING 19
2 Autocorrelation Function and Spectrum of Stationary Processes 21
2.1 Autocorrelation Properties of Stationary Models 21
2.2 Spectral Properties of Stationary Models 34
Appendix A2.1 Link Between the Sample Spectrum and Autocovariance
Function Estimate 43
Exercises 44
3 Linear Stationary Models 47
3.1 General Linear Process 47
3.2 Autoregressive Processes 54
3.3 Moving Average Processes 68
3.4 Mixed Autoregressive--Moving Average Processes 75
Appendix A3.1 Autocovariances Autocovariance Generating Function and Stationarity Conditions for a General Linear Process 82
Appendix A3.2 Recursive Method for Calculating Estimates of Autoregressive Parameters 84
Exercises 86
4 Linear Nonstationary Models 88
4.1 Autoregressive Integrated Moving Average Processes 88
4.2 Three Explicit Forms for the ARIMA Model 97
4.3 Integrated Moving Average Processes 106
Appendix A4.1 Linear Difference Equations 116
Appendix A4.2 IMA(0 1 1) Process with Deterministic Drift 121
Appendix A4.3 ARIMA Processes with Added Noise 122
Exercises 126
5 Forecasting 129
5.1 Minimum Mean Square Error Forecasts and Their Properties 129
5.2 Calculating Forecasts and Probability Limits 135
5.3 Forecast Function and Forecast Weights 139
5.4 Examples of Forecast Functions and Their Updating 144
5.5 Use of State-Space Model Formulation for Exact Forecasting 155
5.6 Summary 162
Appendix A5.1 Correlation Between Forecast Errors 164
Appendix A5.2 Forecast Weights for any Lead Time 166
Appendix A5.3 Forecasting in Terms of the General Integrated Form 168
Exercises 174
PART TWO STOCHASTIC MODEL BUILDING 177
6 Model Identification 179
6.1 Objectives of Identification 179
6.2 Identification Techniques 180
6.3 Initial Estimates for the Parameters 194
6.4 Model Multiplicity 202
Appendix A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process 206
Exercises 207
7 Parameter Estimation 209
7.1 Study of the Likelihood and Sum-of-Squares Functions 209
7.2 Nonlinear Estimation 226
7.3 Some Estimation Results for Specific Models 236
7.4 Likelihood Function Based on the State-Space Model 242
7.5 Estimation Using Bayes' Theorem 245
Appendix A7.1 Review of Normal Distribution Theory 251
Appendix A7.2 Review of Linear Least-Squares Theory 256
Appendix A7.3 Exact Likelihood Function for Moving Average and Mixed Processes 259
Appendix A7.4 Exact Likelihood Function for an Autoregressive Process 266
Appendix A7.5 Asymptotic Distribution of Estimators for Autoregressive Models 274
Appendix A7.6 Examples of the Effect of Parameter Estimation Errors on Variances of Forecast Errors and Probability Limits for Forecasts 277
Appendix A7.7 Special Note on Estimation ofMoving Average Parameters 280
Exercises 280
8 Model Diagnostic Checking 284
8.1 Checking the Stochasti
PREFACE TO THE FOURTH EDITION xxiii
PREFACE TO THE THIRD EDITION xxv
1 Introduction 1
1.1 Five Important Practical Problems 2
1.2 Stochastic and Deterministic Dynamic Mathematical Models 6
1.3 Basic Ideas in Model Building 14
Appendix A1.1 Use of the R Software 17
Exercises 18
PART ONE STOCHASTIC MODELS AND THEIR FORECASTING 19
2 Autocorrelation Function and Spectrum of Stationary Processes 21
2.1 Autocorrelation Properties of Stationary Models 21
2.2 Spectral Properties of Stationary Models 34
Appendix A2.1 Link Between the Sample Spectrum and Autocovariance
Function Estimate 43
Exercises 44
3 Linear Stationary Models 47
3.1 General Linear Process 47
3.2 Autoregressive Processes 54
3.3 Moving Average Processes 68
3.4 Mixed Autoregressive--Moving Average Processes 75
Appendix A3.1 Autocovariances Autocovariance Generating Function and Stationarity Conditions for a General Linear Process 82
Appendix A3.2 Recursive Method for Calculating Estimates of Autoregressive Parameters 84
Exercises 86
4 Linear Nonstationary Models 88
4.1 Autoregressive Integrated Moving Average Processes 88
4.2 Three Explicit Forms for the ARIMA Model 97
4.3 Integrated Moving Average Processes 106
Appendix A4.1 Linear Difference Equations 116
Appendix A4.2 IMA(0 1 1) Process with Deterministic Drift 121
Appendix A4.3 ARIMA Processes with Added Noise 122
Exercises 126
5 Forecasting 129
5.1 Minimum Mean Square Error Forecasts and Their Properties 129
5.2 Calculating Forecasts and Probability Limits 135
5.3 Forecast Function and Forecast Weights 139
5.4 Examples of Forecast Functions and Their Updating 144
5.5 Use of State-Space Model Formulation for Exact Forecasting 155
5.6 Summary 162
Appendix A5.1 Correlation Between Forecast Errors 164
Appendix A5.2 Forecast Weights for any Lead Time 166
Appendix A5.3 Forecasting in Terms of the General Integrated Form 168
Exercises 174
PART TWO STOCHASTIC MODEL BUILDING 177
6 Model Identification 179
6.1 Objectives of Identification 179
6.2 Identification Techniques 180
6.3 Initial Estimates for the Parameters 194
6.4 Model Multiplicity 202
Appendix A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process 206
Exercises 207
7 Parameter Estimation 209
7.1 Study of the Likelihood and Sum-of-Squares Functions 209
7.2 Nonlinear Estimation 226
7.3 Some Estimation Results for Specific Models 236
7.4 Likelihood Function Based on the State-Space Model 242
7.5 Estimation Using Bayes' Theorem 245
Appendix A7.1 Review of Normal Distribution Theory 251
Appendix A7.2 Review of Linear Least-Squares Theory 256
Appendix A7.3 Exact Likelihood Function for Moving Average and Mixed Processes 259
Appendix A7.4 Exact Likelihood Function for an Autoregressive Process 266
Appendix A7.5 Asymptotic Distribution of Estimators for Autoregressive Models 274
Appendix A7.6 Examples of the Effect of Parameter Estimation Errors on Variances of Forecast Errors and Probability Limits for Forecasts 277
Appendix A7.7 Special Note on Estimation ofMoving Average Parameters 280
Exercises 280
8 Model Diagnostic Checking 284
8.1 Checking the Stochasti
Details
| Erscheinungsjahr: | 2015 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 720 |
| Inhalt: | 720 S. |
| ISBN-13: | 9781118675021 |
| ISBN-10: | 1118675029 |
| Sprache: | Englisch |
| Herstellernummer: | 1W118675020 |
| Autor: |
Box, George E. P.
Jenkins, Gwilym M. Reinsel, Gregory C. Ljung, Greta M. |
| Auflage: | 5. Aufl. |
| Hersteller: |
Wiley
Wiley & Sons |
| Maße: | 258 x 184 x 39 mm |
| Von/Mit: | George E. P. Box (u. a.) |
| Erscheinungsdatum: | 07.08.2015 |
| Gewicht: | 1,514 kg |
Warnhinweis