3,375 research outputs found
Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window
Bayesian model averaging has become a widely used approach to accounting for
uncertainty about the structural form of the model generating the data. When
data arrive sequentially and the generating model can change over time, Dynamic
Model Averaging (DMA) extends model averaging to deal with this situation.
Often in macroeconomics, however, many candidate explanatory variables are
available and the number of possible models becomes too large for DMA to be
applied in its original form. We propose a new method for this situation which
allows us to perform DMA without considering the whole model space, but using a
subset of models and dynamically optimizing the choice of models at each point
in time. This yields a dynamic form of Occam's window. We evaluate the method
in the context of the problem of nowcasting GDP in the Euro area. We find that
its forecasting performance compares well that of other methods.
Keywords: Bayesian model averaging; Model uncertainty; Nowcasting; Occam's
window
Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models
Using mathematical modelling tools, we assessed the potential for land use change (LUC) associated with the Intergovernmental Panel on Climate Change low- and high-end emission scenarios (RCP2.6 and RCP8.5) to impact malaria transmission in Africa. To drive a spatially explicit, dynamical malaria model, data from the four available earth system models (ESMs) that contributed to the LUC experiment of the Fifth Climate Model Intercomparison Project are used. Despite the limited size of the ESM ensemble, stark differences in the assessment of how LUC can impact climate are revealed. In three out of four ESMs, the impact of LUC on precipitation and temperature over the next century is limited, resulting in no significant change in malaria transmission. However, in one ESM, LUC leads to increases in precipitation under scenario RCP2.6, and increases in temperature in areas of land use conversion to farmland under both scenarios. The result is a more intense transmission and longer transmission seasons in the southeast of the continent, most notably in Mozambique and southern Tanzania. In contrast, warming associated with LUC in the Sahel region reduces risk in this model, as temperatures are already above the 25-30°C threshold at which transmission peaks. The differences between the ESMs emphasise the uncertainty in such assessments. It is also recalled that the modelling framework is unable to adequately represent local-scale changes in climate due to LUC, which some field studies indicate could be significant
Monitoring for Silent Actions
Silent actions are an essential mechanism for system modelling and specification. They are used to abstractly report the occurrence of computation steps without divulging their precise details, thereby enabling the description of important aspects such as the branching structure of a system. Yet, their use rarely features in specification logics used in runtime verification. We study monitorability aspects of a branching-time logic that employs silent actions, identifying which formulas are monitorable for a number of instrumentation setups. We also consider defective instrumentation setups that imprecisely report silent events, and establish monitorability results for tolerating these imperfections
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