Traditionally, the literature on forecasting exchange rates with many potential predictors have primarily only
accounted for parameter uncertainty using Bayesian Model Averaging (BMA). Though BMA-based models
of exchange rates tend to outperform the random walk model, we show that when accounting for model
uncertainty over and above parameter uncertainty through the use of Dynamic model Averaging (DMA), the
gains relative to the random walk model are even bigger. That is, DMA models outperform not only the
random walk model, but also the BMA model of exchange rates. We obtain these results based on fifteen
potential predictors used to forecast two South African Rand-based exchange rates. In the process, we also
unveil variables, which tends to vary over time, that are good predictors of the Rand-Dollar and Rand-Pound
exchange rates at different forecasting horizons.
Since the emergence of systematic science it has been recognized that a natural phenomenon can be described
by different models that vary in their complexity and their ability to capture the details of the features
Olivier, Laurentz Eugene; Craig, Ian K.(Elsevier, 2013-02)
The performance of a model predictive controller depends on the quality of
the plant model that is available. Often parameters in a run-of-mine (ROM)
ore milling circuit are uncertain and inaccurate parameter estimation ...
Sekgota, Mpolaeng Gilbert(University of Pretoria, 2013-05-27)
The Sustainable Restitution Support – South Africa (SRS-SA) program aimed at the development of a post-settlement support model that could be used to support beneficiaries of land reform in South Africa, especially those ...