In this paper we study the impact of model uncertainty, which occurs when linking a stress scenario to default probabilities, on reduced-form credit risk stress testing. This type of uncertainty is omnipresent in most macroeconomic stress testing applications due to short time series for banks'...

This paper studies model selection consistency for high dimensional sparse regression when data exhibits both cross-sectional and serial dependency. Most commonly-used model selection methods fail to consistently recover the true model when the covariates are highly correlated. Motivated by...

This paper tests the ability of popular New Keynesian models, which are traditionally used to study monetary policy and business cycles, to match the data regarding a key channel for monetary transmission: the dynamic interactions between macroeconomic variables and their corresponding...

To find out whether the model misspecification matters for hedging accuracy, we carefully select six increasingly complicated asset models, i.e., the Black-scholes (BS) model, the Merton model (M), the Heston (H) model, the Heston jump-diffusion model (HJ), the double Heston (dbH) model and the...

We estimate a Markov-switching mixture of two familiar macroeconomic models: a richly parameterized dynamic stochastic general equilibrium (DSGE) model and a corresponding Bayesian vector autoregression (BVAR) model. We show that the Markov-switching mixture model dominates both individual...

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities...

We compare sparse and dense representations of predictive models in macroeconomics, microeconomics and ftnance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate...

We take a Bayesian approach to model selection in regression models with structural breaks in conditional mean and residual variance parameters. A novel feature of our approach is that it does not assume knowledge of the parameter subset that undergoes structural breaks, but instead conducts...

The recent increase in the breath of computational methodologies has been matched with a corresponding increase in the difficulty of comparing the relative explanatory power of models from different methodological lineages. In order to help address this problem a universal information criterion...