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A new Bayesian approach to quantile autoregressive time series model estimation and forecasting

Yuzhi Cai Orcid Logo, Julian Stander, Neville Davies

Journal of Time Series Analysis, Volume: 33, Issue: 4, Pages: 684 - 698

Swansea University Author: Yuzhi Cai Orcid Logo

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DOI (Published version): 10.1111/j.1467-9892.2012.00800.x

Abstract

This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimation andforecasting. We establish that the joint posterior distribution of the model parameters and future values is welldefined. The associated Markov chain Monte Carlo algorithm for parameter estimatio...

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Published in: Journal of Time Series Analysis
ISSN: 0143-9782
Published: wileyonlinelibrary.com 2012
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URI: https://cronfa.swan.ac.uk/Record/cronfa11711
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Abstract: This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimation andforecasting. We establish that the joint posterior distribution of the model parameters and future values is welldefined. The associated Markov chain Monte Carlo algorithm for parameter estimation and forecasting convergesto the posterior distribution quickly. We also present a combining forecasts technique to produce more accurateout-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to checkthe quality of the estimated conditional quantiles is developed. We verify our methodology using simulationstudies and then apply it to currency exchange rate data. The results obtained show that an unequally weightedcombining method performs better than other forecasting methodology.
Keywords: Combining forecasts; MCMC; quantile modelling; quantile forecasting; predictive density functions
College: Faculty of Humanities and Social Sciences
Issue: 4
Start Page: 684
End Page: 698

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