  We propose a general dynamic model averaging (DMA) approach based on
Markov-Chain Monte Carlo for the sequential combination and estimation of
quantile regression models with time-varying parameters. The efficiency and the
effectiveness of the proposed DMA approach and the MCMC algorithm are shown
through simulation studies and applications to macro-economics and finance.
