A smoothing spline is considered to propose a novel model for the stochastic 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 stochastic 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.
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