Adaptive Dynamic Model Averaging with an Application to House Price Forecasting

Understand

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.