Time Series Analysis

Detalles Bibliográficos
Autor principal: Palma, Wilfredo
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2016.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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