Bayesian Analysis of Time-Varying Quantiles Using a Smoothing Spline
AbstractA 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-845.
Length: 29 pages
Date of creation: Mar 2012
Date of revision:
Contact details of provider:
Postal: Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033
Web page: http://www.cirje.e.u-tokyo.ac.jp/index.html
More information through EDIRC
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-04-10 (All new papers)
- NEP-ECM-2012-04-10 (Econometrics)
- NEP-ETS-2012-04-10 (Econometric Time Series)
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CIRJE administrative office).
If references are entirely missing, you can add them using this form.