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• Cover
• Title Page
• Copyright
• PREFACE
• ACKNOWLEDGMENTS
• ACRONYMS
• ABOUT THE COMPANION WEBSITE
• CHAPTER 1: INTRODUCTION
• 1.1 TIME SERIES DATA
• 1.2 RANDOM VARIABLES AND STATISTICAL MODELING
• 1.3 DISCRETE-TIME MODELS
• 1.4 SERIAL DEPENDENCE
• 1.5 NONSTATIONARITY
• 1.6 WHITENESS TESTING
• 1.7 PARAMETRIC AND NONPARAMETRIC MODELING
• 1.8 FORECASTING
• 1.9 TIME SERIES MODELING
• 1.10 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 2: LINEAR PROCESSES
• 2.1 DEFINITION
• 2.2 STATIONARITY
• 2.3 INVERTIBILITY
• 2.4 CAUSALITY
• 2.5 REPRESENTATIONS OF LINEAR PROCESSES
• 2.6 WEAK AND STRONG DEPENDENCE
• 2.7 ARMA MODELS
• 2.8 AUTOCOVARIANCE FUNCTION
• 2.9 ACF AND PARTIAL ACF FUNCTIONS
• 2.10 ARFIMA PROCESSES
• 2.11 FRACTIONAL GAUSSIAN NOISE
• 2.12 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 3: STATE SPACE MODELS
• 3.1 INTRODUCTION
• 3.2 LINEAR DYNAMICAL SYSTEMS
• 3.3 STATE SPACE MODELING OF LINEAR PROCESSES
• 3.4 STATE ESTIMATION
• 3.5 EXOGENOUS VARIABLES
• 3.6 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 4: SPECTRAL ANALYSIS
• 4.1 TIME AND FREQUENCY DOMAINS
• 4.2 LINEAR FILTERS
• 4.3 SPECTRAL DENSITY
• 4.4 PERIODOGRAM
• 4.5 SMOOTHED PERIODOGRAM
• 4.6 EXAMPLES
• 4.7 WAVELETS
• 4.8 SPECTRAL REPRESENTATION
• 4.9 TIME-VARYING SPECTRUM
• 4.10 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 5: ESTIMATION METHODS
• 5.1 MODEL BUILDING
• 5.2 PARSIMONY
• 5.3 AKAIKE AND SCHWARTZ INFORMATION CRITERIA
• 5.4 ESTIMATION OF THE MEAN
• 5.5 ESTIMATION OF AUTOCOVARIANCES
• 5.6 MOMENT ESTIMATION
• 5.7 MAXIMUM-LIKELIHOOD ESTIMATION
• 5.8 WHITTLE ESTIMATION
• 5.9 STATE SPACE ESTIMATION
• 5.10 ESTIMATION OF LONG-MEMORY PROCESSES
• 5.11 NUMERICAL EXPERIMENTS
• 5.12 BAYESIAN ESTIMATION
• 5.13 STATISTICAL INFERENCE
• 5.14 ILLUSTRATIONS
• 5.15 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 6: NONLINEAR TIME SERIES
• 6.1 INTRODUCTION
• 6.2 TESTING FOR LINEARITY
• 6.3 HETEROSKEDASTIC DATA
• 6.4 ARCH MODELS
• 6.5 GARCH MODELS
• 6.6 ARFIMA-GARCH MODELS
• 6.7 ARCH(∞) MODELS
• 6.8 APARCH MODELS
• 6.9 STOCHASTIC VOLATILITY
• 6.10 NUMERICAL EXPERIMENTS
• 6.11 DATA APPLICATIONS
• 6.12 VALUE AT RISK
• 6.13 AUTOCORRELATION OF SQUARES
• 6.14 THRESHOLD AUTOREGRESSIVE MODELS
• 6.15 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 7: PREDICTION
• 7.1 OPTIMAL PREDICTION
• 7.2 ONE-STEP AHEAD PREDICTORS
• 7.3 MULTISTEP AHEAD PREDICTORS
• 7.4 HETEROSKEDASTIC MODELS
• 7.5 PREDICTION BANDS
• 7.6 DATA APPLICATION
• 7.7 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 8: NONSTATIONARY PROCESSES
• 8.1 INTRODUCTION
• 8.2 UNIT ROOT TESTING
• 8.3 ARIMA PROCESSES
• 8.4 LOCALLY STATIONARY PROCESSES
• 8.5 STRUCTURAL BREAKS
• 8.6 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 9: SEASONALITY
• 9.1 SARIMA MODELS
• 9.2 SARFIMA MODELS
• 9.3 GARMA MODELS
• 9.4 CALCULATION OF THE ASYMPTOTIC VARIANCE
• 9.5 AUTOCOVARIANCE FUNCTION
• 9.6 MONTE CARLO STUDIES
• 9.7 ILLUSTRATION
• 9.8 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 10: TIME SERIES REGRESSION
• 10.1 MOTIVATION
• 10.2 DEFINITIONS
• 10.3 PROPERTIES OF THE LSE
• 10.4 PROPERTIES OF THE BLUE
• 10.5 ESTIMATION OF THE MEAN
• 10.6 POLYNOMIAL TREND
• 10.7 HARMONIC REGRESSION
• 10.8 ILLUSTRATION: AIR POLLUTION DATA
• 10.9 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 11: MISSING VALUES AND OUTLIERS
• 11.1 INTRODUCTION
• 11.2 LIKELIHOOD FUNCTION WITH MISSING VALUES
• 11.3 EFFECTS OF MISSING VALUES ON ML ESTIMATES
• 11.4 EFFECTS OF MISSING VALUES ON PREDICTION
• 11.5 INTERPOLATION OF MISSING DATA
• 11.6 SPECTRAL ESTIMATION WITH MISSING VALUES
• 11.7 OUTLIERS AND INTERVENTION ANALYSIS
• 11.8 BIBLIOGRAPHIC NOTES
• Problems
• CHAPTER 12: NON-GAUSSIAN TIME SERIES
• 12.1 DATA DRIVEN MODELS
• 12.2 PARAMETER DRIVEN MODELS
• 12.3 ESTIMATION
• 12.4 DATA ILLUSTRATIONS
• 12.5 ZERO-INFLATED MODELS
• 12.6 BIBLIOGRAPHIC NOTES
• Problems
• APPENDIX A: COMPLEMENTS
• A.1 PROJECTION THEOREM
• A.2 WOLD DECOMPOSITION
• A.3 BIBLIOGRAPHIC NOTES
• APPENDIX B: SOLUTIONS TO SELECTED PROBLEMS
• APPENDIX C: DATA AND CODES
• REFERENCES
• TOPIC INDEX
• AUTHOR INDEX
• End User License Agreement