Time series: Advanced methods

Wai Keung LI, Howell TONG

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Recent developments in the time-domain analysis of time series are reviewed. The concept of dynamical systems serves as a unifying theme of the review. We consider first methods for the modelling of the drift component or the conditional mean of a time series. This includes the class of threshold models and its variants. We then consider methods for the modelling of the diffusion component or the conditional variance of a time series which includes the popular generalized autoregressive conditional heteroscedastic G(ARCH) models and the stochastic volatility (SV) models. Hybrid models for the modelling of both the drift and the diffusion are then introduced. Long memory and discrete-valued time series models are also included. The main focus is on univariate series although multivariate series are also mentioned where appropriate. Copyright © 2015 Elsevier Ltd. All rights reserved.
Original languageEnglish
Title of host publicationInternational encyclopedia of the social & behavioral sciences
EditorsJames D. WRIGHT
Place of PublicationAmsterdam, Netherlands
PublisherElsevier
Pages311-315
Volume24
Edition2nd
ISBN (Print)9780080970875
DOIs
Publication statusPublished - 2015

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Time series
Modeling
Time Domain Analysis
Conditional Variance
Threshold Model
Stochastic Volatility Model
Series
Long Memory
Hybrid Model
Time Series Models
Univariate
Dynamical system
Model
Concepts
Class
Review

Citation

Li, W. K., & Tong, H. (2015). Time series: Advanced methods. In J. D. Wright (Ed.), International encyclopedia of the social & behavioral sciences (2nd ed., Vol. 24, pp. 311-315). Amsterdam, Netherlands: Elsevier.

Keywords

  • Autoregressive
  • Heteroscedastic
  • Stochastic volatility
  • Time series