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A Bayesian method for non-Gaussian autoregressive quantile function time series models

Presented by: 
Y Cai [Plymouth]
Date: 
Wednesday 18th June 2008 - 15:30 to 16:10
Venue: 
INI Seminar Room 1
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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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons