Model Uncertainty

Model Uncertainty

The evolution of Bayesian approaches for model uncertainty over the past decade has been remarkable. Catalyzed by advances in methods and technology for posterior computation, the scope of these methods has widened substantially. Major thrusts of these developments have included new methods for semi-automatic prior specification and posterior exploration. To illustrate key aspects of this evolu...

Model uncertainty

In statistical exercises where there are several candidate models, the traditional approach is to select one model using some data driven criterion and use that model for estimation, testing and other purposes, ignoring the variability of the model selection process. We discuss some problems associated with this approach. An alternative scheme is to use a model-averaged estimator, that is, a we...

Climate Model Uncertainty

Macroeconomics and Model Uncertainty

This paper provides some reflections on the new macroeconomics of model uncertainty. Model uncertainty is argued to have important positive and normative implications for macroeconomic analysis. We provide a general discussion of the relationship between macroeconomic models that assume a specific model and those that do not and provide some examples from the existing literature. Our discussion...

Roughness uncertainty analysis in river flooding using HEC-RAS model

Although flood maps based on the deterministic approach play an important role in minimizing flood losses, there is considerable uncertainty in calculating the level of water inundation. Roughness is a key parameter in water surface elevation. Since roughness is not easily measurable and is estimated based on experimental and laboratory methods, it introduces a significant degree of uncertainty...