BAYESIAN THRESHOLD AUTOREGRESSIVE MODELS FOR NONLINEAR TIME SERIES

John Geweke; Nobuhiko Terui

doi:10.1111/j.1467-9892.1993.tb00156.x

BAYESIAN THRESHOLD AUTOREGRESSIVE MODELS FOR NONLINEAR TIME SERIES

John Geweke, Nobuhiko Terui

Research output: Contribution to journal › Article › peer-review

27 Citations (Scopus)

Abstract

Abstract. This paper provides a Bayesian approach to statistical inference in the threshold autoregressive model for time series. The exact posterior distribution of the delay and threshold parameters is derived, as is the multi‐step‐ahead predictive density. The proposed methods are applied to the Wolfe's sunspot and Canadian lynx data sets.

Original language	English
Pages (from-to)	441-454
Number of pages	14
Journal	Journal of Time Series Analysis
Volume	14
Issue number	5
DOIs	https://doi.org/10.1111/j.1467-9892.1993.tb00156.x
Publication status	Published - 1993 Sept
Externally published	Yes

Keywords

Canadian lynx
Monte Carlo integration
Nonlinear time series
Wolfe's sunspot
regime change prediction
threshold model

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1111/j.1467-9892.1993.tb00156.x

Cite this

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title = "BAYESIAN THRESHOLD AUTOREGRESSIVE MODELS FOR NONLINEAR TIME SERIES",

abstract = "Abstract. This paper provides a Bayesian approach to statistical inference in the threshold autoregressive model for time series. The exact posterior distribution of the delay and threshold parameters is derived, as is the multi‐step‐ahead predictive density. The proposed methods are applied to the Wolfe's sunspot and Canadian lynx data sets.",

keywords = "Canadian lynx, Monte Carlo integration, Nonlinear time series, Wolfe's sunspot, regime change prediction, threshold model",

author = "John Geweke and Nobuhiko Terui",

year = "1993",

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AU - Geweke, John

AU - Terui, Nobuhiko

PY - 1993/9

Y1 - 1993/9

N2 - Abstract. This paper provides a Bayesian approach to statistical inference in the threshold autoregressive model for time series. The exact posterior distribution of the delay and threshold parameters is derived, as is the multi‐step‐ahead predictive density. The proposed methods are applied to the Wolfe's sunspot and Canadian lynx data sets.

AB - Abstract. This paper provides a Bayesian approach to statistical inference in the threshold autoregressive model for time series. The exact posterior distribution of the delay and threshold parameters is derived, as is the multi‐step‐ahead predictive density. The proposed methods are applied to the Wolfe's sunspot and Canadian lynx data sets.

KW - Canadian lynx

KW - Monte Carlo integration

KW - Nonlinear time series

KW - Wolfe's sunspot

KW - regime change prediction

KW - threshold model

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DO - 10.1111/j.1467-9892.1993.tb00156.x

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VL - 14

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JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

IS - 5

ER -

BAYESIAN THRESHOLD AUTOREGRESSIVE MODELS FOR NONLINEAR TIME SERIES

Abstract

Keywords

ASJC Scopus subject areas

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