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Bayesian Analysis of Stochastic Quantiles Using a Smoothing Spline

Author

Listed:
  • Yuta Kurose

    (Graduate School of Economics, University of Tokyo)

  • Yasuhiro Omori

    (Faculty of Economics, University of Tokyo)

Abstract

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.

Suggested Citation

  • Yuta Kurose & Yasuhiro Omori, 2011. "Bayesian Analysis of Stochastic Quantiles Using a Smoothing Spline," CIRJE F-Series CIRJE-F-798, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2011cf798
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