  Dynamic model averaging (DMA) combines the forecasts of a large number of
dynamic linear models (DLMs) to predict the future value of a time series. The
performance of DMA critically depends on the appropriate choice of two
forgetting factors. The first of these controls the speed of adaptation of the
coefficient vector of each DLM, while the second enables time variation in the
model averaging stage. In this paper we develop a novel, adaptive dynamic model
averaging (ADMA) methodology. The proposed methodology employs a stochastic
optimisation algorithm that sequentially updates the forgetting factor of each
DLM, and uses a state-of-the-art non-parametric model combination algorithm
from the prediction with expert advice literature, which offers finite-time
performance guarantees. An empirical application to quarterly UK house price
data suggests that ADMA produces more accurate forecasts than the benchmark
autoregressive model, as well as competing DMA specifications.
