Bayesian Analysis of Time-Varying Quantiles Using a Smoothing Spline

Yuta Kurose and Yasuhiro Omori ()
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Yuta Kurose: Graduate School of Economics, University of Tokyo

No CIRJE-F-845, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: 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.

Pages: 29 pages
Date: 2012-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2012cf845

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