  A new class of models, named dynamic quantile linear models, is presented. It
combines dynamic linear models with distribution free quantile regression
producing a robust statistical method. Bayesian inference for dynamic quantile
linear models can be performed using an efficient Markov chain Monte Carlo
algorithm. A fast sequential procedure suited for high-dimensional predictive
modeling applications with massive data, in which the generating process is
itself changing overtime, is also proposed. The proposed model is evaluated
using synthetic and well-known time series data. The model is also applied to
predict annual incidence of tuberculosis in Rio de Janeiro state for future
years and compared with global strategy targets set by the World Health
Organization.
