We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a large cross-section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coefficients and then implicitly...

Empirical work on bank loans typically regresses loan spreads (markups of loan interest rates over a benchmark rate) on observed characteristics of banks, firms, and loans. The estimation is problematic when some of these characteristics are only partially observed and the matching of banks and...

Firms face a continuous process of technological and environmental changes that implies making managerial decisions in a dynamic context. However, costs and other constraints prevent firms from making instant adjustments towards optimal conditions and may cause inefficiency to be persistent in...

Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model,...

Bayesian hierarchical models serve as a standard methodology for aggregation and synthesis, used widely in statistics and other disciplines. I use this framework to aggregate the data from seven randomised experiments of expanding access to microcredit, assessing both the general impact of the...

This article analyzes the impact of capital structure and bank liquidity on the performance of commercial banks in Vietnam, a transition market in Asia. This research is unique because it is the first study to employ the Bayesian Estimation methods in banking studies. The data includes 463...

The theoretical literature gives conflicting predictions on how bank competition should affect financial stability, and dozens of researchers have attempted to evaluate the relationship empirically. We collect 598 estimates of the competitionstability nexus reported in 31 studies and analyze the...

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 models the housing sector, mortgages and endogenous default in a DSGE setting with nominal and real rigidities. We use data for the period 1981-2006 to estimate our model using Bayesian techniques. We analyze how an increase in risk in the mortgage market raises the default rate and...

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...