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Time Series Analysis.

By: Palma, Wilfredo.
Material type: materialTypeLabelBookSeries: New York Academy of Sciences Ser: Publisher: Newark : John Wiley & Sons, Incorporated, 2016Copyright date: �2016Description: 1 online resource (629 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9781118634349.Genre/Form: Electronic books.Online resources: Click to View
Contents:
Intro -- Title Page -- Copyright -- Table of Contents -- 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.
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Intro -- Title Page -- Copyright -- Table of Contents -- 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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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