A0862
Title: Dynamic Bayesian regression quantile synthesis for forecasting outlook-at-risk
Authors: Genya Kobayashi - School of Commerce, Meiji University (Japan) [presenting]
Yuta Yamauchi - Nagoya University (Japan)
Shonosuke Sugasawa - Keio University (Japan)
Dongu Han - Korea University (Korea, South)
Abstract: The aim is to provide a Bayesian approach to accurate quantile forecasting for time series data through the Bayesian predictive synthesis. The proposed dynamic Bayesian regression quantile introduces predictions from the agent quantile predictive models as latent factors and lets the weights for the agent models vary across time, constituting a dynamic latent factor model for quantiles. It is also considered to extend the model for quantile prediction of multiple time series data by introducing an additional factor structure to the synthesis weights. The performance of the proposed approach is demonstrated using the US inflation rate and GDP growth rates for some developed countries.
