Cover Image

Time series analysis / Wilfredo Palma, Ponticia Universidad Católica de Chile.

Show other versions (1)

A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univar...

Full description

Saved in:
Bibliographic Details
Online Access: Full Text (via ProQuest)
Main Author: Palma, Wilfredo, 1963-
Format: eBook
Language:English
Published: Hoboken, NJ : John Wiley & Sons, [2016]
Subjects:
Time-series analysis.
Table of Contents:
  • 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.

Similar Items