期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 113, 期 522, 页码 675-685出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/01621459.2016.1273117
关键词
Bayesian nonparametrics; Density forecast; Forecast calibration; Forecast combination; Posterior consistency
资金
- European Union, Seventh Framework Programme FP7/2007-2013 [SYRTO-SSH-2012-320270]
- Institut Europlace of Finance, Systemic Risk grant
- Global Risk Institute in Financial Services
- Louis Bachelier Institute, Systemic Risk Research Initiative
- Italian Ministry of Education, University and Research (MIUR) PRIN 2010-11 grant MISURA
- INdAm-GNAMPA Project 2016
- Italian Ministry of Education, University and Research (MIUR), PRIN 2015
We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting, we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on combination Gibbs and slice sampling. We provide some conditions under which the proposed probabilistic calibration converges in terms of weak posterior consistency to the true underlying density for both cases of iid and Markovian observations. This calibration property improves upon the earlier calibration approaches. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据