Willkommen, schön sind Sie da!
Logo Ex Libris

A Course in Time Series Analysis

  • E-Book (pdf)
  • 496 Seiten
(0) Erste Bewertung abgeben
Bewertungen
(0)
(0)
(0)
(0)
(0)
Alle Bewertungen ansehen
New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to buil... Weiterlesen
E-Books ganz einfach mit der kostenlosen Ex Libris-Reader-App lesen. Hier erhalten Sie Ihren Download-Link.
CHF 161.00
Download steht sofort bereit
Informationen zu E-Books
E-Books eignen sich auch für mobile Geräte (sehen Sie dazu die Anleitungen).
E-Books von Ex Libris sind mit Adobe DRM kopiergeschützt: Erfahren Sie mehr.
Weitere Informationen finden Sie hier.

Beschreibung

New statistical methods and future directions of research in time series
A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:

  • Contributions from eleven of the world???s leading figures in time series
  • Shared balance between theory and application
  • Exercise series sets
  • Many real data examples
  • Consistent style and clear, common notation in all contributions
  • 60 helpful graphs and tables

    Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.

    An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

    Autorentext
    DANIEL PEÑA, PhD, is Professor of Statistics, Universidad Carlos III de Madrid.

    GEORGE C. TIAO, PhD, is W. Allen Wallis Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.

    RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.



    Klappentext
    New statistical methods and future directions of research in time series

    A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:

    • Contributions from eleven of the world's leading figures in time series
    • Shared balance between theory and application
    • Exercise series sets
    • Many real data examples
    • Consistent style and clear, common notation in all contributions
    • 60 helpful graphs and tables

    Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.



    Inhalt

    1. Introduction 1
    D. Pena and G. C. Tiao

    1.1. Examples of time series problems, 1

    1.1.1. Stationary series, 2

    1.1.2. Nonstationary series, 3

    1.1.3. Seasonal series, 5

    1.1.4. Level shifts and outliers in time series, 7

    1.1.5. Variance changes, 7

    1.1.6. Asymmetric time series, 7

    1.1.7. Unidirectional-feedback relation between series, 9

    1.1.8. Comovement and cointegration, 10

    1.2. Overview of the book, 10

    1.3. Further reading, 19

    PART I BASIC CONCEPTS IN UNIVARIATE TIME SERIES

    2. Univariate Time Series: Autocorrelation, Linear Prediction, Spectrum, and State-Space Model 25
    G. T. Wilson

    2.1. Linear time series models, 25

    2.2. The autocorrelation function, 28

    2.3. Lagged prediction and the partial autocorrelation function, 33

    2.4. Transformations to stationarity, 35

    2.5. Cycles and the periodogram, 37

    2.6. The spectrum, 42

    2.7. Further interpretation of time series acf, pacf, and spectrum, 46

    2.8. State-space models and the Kalman Filter, 48

    3. Univariate Autoregressive Moving-Average Models 53
    G. C. Tiao

    3.1. Introduction, 53

    3.1.1. Univariate ARMA models, 54

    3.1.2. Outline of the chapter, 55

    3.2. Some basic properties of univariate ARMA models, 55

    3.2.1. The ø and TT weights, 56

    3.2.2. Stationarity condition and autocovariance structure o f z 58

    3.2.3. The autocorrelation function, 59

    3.2.4. The partial autocorrelation function, 60

    3.2.5. The extended autocorrelaton function, 61

    3.3. Model specification strategy, 63

    3.3.1. Tentative specification, 63

    3.3.2. Tentative model specification via SEACF, 67

    3.4. Examples, 68

    4. Model Fitting and Checking, and the Kalman Filter 86
    G. T. Wilson

    4.1. Prediction error and the estimation criterion, 86

    4.2. The likelihood of ARMA models, 90

    4.3. Likelihoods calculated using orthogonal errors, 94

    4.4. Properties of estimates and problems in estimation, 98

    4.5. Checking the fitted model, 101

    4.6. Estimation by fitting to the sample spectrum, 104

    4.7. Estimation of structural models by the Kalman filter, 105

    5. Prediction and Model Selection 111
    D. Pefia

    5.1. Introduction, 111

    5.2. Properties of minimum mean-square error prediction, 112

    5.2.1. Prediction by the conditional expectation, 112

    5.2.2. Linear predictions, 113

    5.3. The computation of ARIMA forecasts, 114

    5.4. Interpreting the forecasts from ARIMA models, 116

    5.4.1. Nonseasonal models, 116

    5.4.2. Seasonal models, 120

    5.5. Prediction confidence intervals, 123

    5.5.1. Known parameter values, 123

    5.5.2. Unknown parameter values, 124

    5.6. Forecast updating, 125

    5.6.1. Computing updated forecasts, 125

    5.6.2. Testing model stability, 125

    5.7. The combination of forecasts, 129

    5.8. Model selection criteria, 131

    5.8.1. The FPE and AIC criteria, 131

    5.8.2. The Schwarz criterion, 133

    5.9. Conclusions, 133

    6. Outliers, Influential Observations, and Missing Data 136
    D. Pena

    6.1. Introduction, 136

    6.2. Types of outliers in time series, 138

    6.2.1. Additive outliers, 138

    6.2.2. Innovative outliers, 141

    6.2.3. Level shifts, 143

    6.2.4. Outliers and intervention analysis, 146

    6.3. Procedures for outlier identification and estimation, 147

    6.3.1. Estimation of outlier effects, 148

    6.3.2. Testing for outliers, 149

    6.4. Influential observations, 152

    6.4.1. Influence on time series, 152

    6.4.2. Influential observations and outliers, 153

    6.5. Multiple outliers, 154

    6.5.1. Masking effects, 154

    6.5.2. Procedures for multiple outlier identification, 156

    6.6. Missing-value estimation, 160

    6.6.1. Optimal interpolation and inverse autocorrelation function, 160

    6.6.2. Estimation of missing values, 162

    6.7. Forecasting with outliers, 164

    6.8. Other approaches, 166

    6.9. Appendix, 166

    7. Automatic Modeling Methods for Univariate Series 171
    V. Gomez and A. Maravall

    7.1. Classical model identification methods, 171

    7.1.1. Subjectivity of the classical methods, 172

    7.1.2. The difficulties with mixed ARMA models, 173

    7.2. Automatic model identification methods, 173

    7.2.1. Unit root testing, 174

    7.2.2. Penalty function methods, 174

    7.2.3. Pattern identification methods, 175

    7.2.4. Uniqueness of the solution and the purpose of modeling, 176

    7.3. Tools for automatic model identification, 177

    7.3.1. Test for the log-level specification, 177

    7.3.2. Re...

  • Produktinformationen

    Titel: A Course in Time Series Analysis
    Autor:
    EAN: 9781118031223
    ISBN: 978-1-118-03122-3
    Digitaler Kopierschutz: Adobe-DRM
    Format: E-Book (pdf)
    Herausgeber: Wiley-Interscience
    Genre: Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
    Anzahl Seiten: 496
    Veröffentlichung: 25.01.2011
    Jahr: 2011
    Untertitel: Englisch
    Dateigrösse: 19.8 MB