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 exploits a recent and granular data set for 1,500 German LSIs to conduct a residential mortgage stress testing exercise. To account for model uncertainty when modeling PD dynamics we use a benchmark-constrained Bayesian model averaging approach that combines standard BMA with a...

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying....

This work calculates coefficients of spatial inequalities of wealth amongst Brazilian municipalities, for each year for the period from 1920 to 2016, states of the federation, from 1939 to 2017, and amongst municipalities from each of the five Brazilians' macro-regions, from 1920 to 2016. The...

The goal of this paper is to evaluate the results of regional economic growth estimates at multiple spatial scales using spatial panel data models. The spatial scales examined are minimum comparable areas, micro-regions, meso-regions and states over the period between 1970 and 2000. Alternative...

In the paper we analyze the convergence process of the West German Laender from 1970 to 1995 using descriptive tools as well as panel estimation methods. Although there have been some winners in this process, the main finding is that convergence was insufficient in the sense that no gains have...

This paper presents a novel Bayesian method for estimating dynamic stochastic general equilibrium (DSGE) models subject to a constrained posterior distribution of the implied Sharpe ratio. We apply our methodology to a DSGE model with habit formation in consumption and leisure, using an estimate...

This paper considers factor estimation from heterogenous data, where some of the variables are noisy and only weakly informative for the factors. To identify the irrelevant variables, we search for zero rows in the loadings matrix of the factor model. To sharply separate these irrelevant...

We propose a Bayesian approach to dynamic panel estimation in the presence of cross-sectional dependence and dynamic heterogeneity which is suitable for inference in short panels, unlike alternative estimators. Monte Carlo simulations indicate that our estimator produces less bias, and a lower...