CIRJE-F-845 "Bayesian Analysis of Time-Varying Quantiles Using a Smoothing Spline"
Author Name

Kurose, Yuta and Yasuhiro Omori

Date March 2012
Full Paper   PDF file
Remarks Revised version of CIRJE-F-798 (2011); subsequently published in Journal of Japan Statistical Society, 42-1, 23-46 (June 2012).
  A smoothing spline is considered to propose a novel model for the time-varying quantile of the univariate time series using a state space approach. A correlation is further incorporated between the dependent variable and its one-step-ahead quantile. Using a Bayesian approach, an efficient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates simultaneously latent time-varying quantiles. Numerical examples are provided to show its high sampling efficiency in comparison with the simple algorithm that generates one latent quantile at a time given other latent quantiles. Furthermore, using Japanese inflation rate data, an empirical analysis is provided with the model comparison.