On a logistic mixture autoregressive model

C. S. WONG, Wai Keung LI

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

We generalise the mixture autoregressive, MAR, model to the logistic mixture autoregressive with exogenous variables, LMARX, model for the modelling of nonlinear time series. The models consist of a mixture of two Gaussian transfer function models with the mixing proportions changing over time. The model can also be considered as a generalisation of the self‐exciting threshold autoregressive, SETAR, model and the open‐loop threshold autoregressive, TARSO, model. The advantages of the LMARX model over other nonlinear time series models include a wider range of shape‐changing predictive distributions, the ability to handle cycles and conditional heteroscedasticity in the time series and better point prediction. Estimation is easily done via a simple EM algorithm and the model selection problem is addressed. The models are applied to two real datasets and compared with other competing models. Copyright © 2001 Biometrika Trust.
Original languageEnglish
Pages (from-to)833-846
JournalBiometrika
Volume88
Issue number3
DOIs
Publication statusPublished - Oct 2001

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Autoregressive Model
Mixture Model
Logistics
Threshold Autoregressive Model
Model
Time series
time series analysis
Logistic Models
Conditional Heteroscedasticity
Nonlinear Time Series Model
Nonlinear Time Series
Predictive Distribution
Mars
Autoregressive model
Gaussian Function
EM Algorithm
Model Selection
Transfer Function
heteroskedasticity
Proportion

Citation

Wong, C. S., & Li, W. K. (2001). On a logistic mixture autoregressive model. Biometrika, 88(3), 833-846. doi: 10.1093/biomet/88.3.833

Keywords

  • EM algorithm
  • Forecasting
  • Mixture mode
  • Model selection