A Bayesian method for non-Gaussian autoregressive quantile function time series models
About this item
| Description: |
Cai, Y (Plymouth)
Wednesday 18 June 2008, 15:30-16:10 Inference and Estimation in Probabilistic Time-Series Models |
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| Created: | 2008-06-24 12:48 | ||||
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| Collection: |
Statistical Theory and Methods for Complex, High-Dimensional Data
Last week's top 10 Top Ten Isaac Newton Institute media items |
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| Publisher: | Isaac Newton Institute | ||||
| Copyright: | Cai, Y | ||||
| Language: | eng (English) | ||||
| Credits: |
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| Abstract: | Many time series in economics and finance are non-Gaussian. In this paper, we propose a Bayesian approach to non-Gaussian autoregressive quantile function time series models where the scale parameter of the models does not depend on the values of the time series. This approach is parametric. So we also compare the proposed parametric approach with the semi-parametric approach (Koenker, 2005). Simulation study and applications to real time series show that the method works very well. |
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