This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting in ation with the ability of quantile regression to...

We investigate an impact of oil-price shocks on GDP and exchange rate dynamics in resource-heterogeneous economies. We employ a Markov regime-switching version of a vector autoregressive (VAR) model to allow for regime shifts, non-linear effects and timevarying parameters of the VAR process....

We propose a method to incorporate information from Dynamic Stochastic General Equilibrium (DSGE) models into Dynamic Factor Analysis. The method combines a procedure previously applied for Bayesian Vector Autoregressions and a Gibbs Sampling approach for Dynamic Factor Models. The factors in...

We construct a “Google Recession Index” (GRI) using Google Trends data on internet search popularity, which tracks the public's attention to recession-related keywords in real time. We then compare nowcasts made with and without this index using both a standard dynamic factor model and a...

This paper investigates the impact of agents' expectations about future fundamental economic disturbances (news) on macroeconomic dynamics. Several intuitive tests provide insight into the information content of the yield curve and its' ability to identify these 'news' disturbances. Bayesian...

We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian techniques for estimation and inference. To improve the sparse recovery ability of the model, we also consider a Group Lasso with a spike-and-slab...

In banking practice, rating transition matrices have become the standard approach of deriving multi-year probabilities of default (PDs) from one-year PDs, the latter normally being available from Basel ratings. Rating transition matrices have gained in importance with the newly adopted IFRS 9...

This paper proposes a simple technical approach for the analytical derivation of Point-in-Time PD (probability of default) forecasts, with minimal data requirements. The inputs required are the current and future Through-the-Cycle PDs of the obligors, their last known default rates, and a...

This paper studies the time-varying parameter (TVP) regression model in which the regression coefficients are random walk latent states with time dependent conditional variances. This TVP model is flexible to accommodate a wide variety of timevariation patterns but requires effective shrinkage...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting in ation with the ability of quantile regression to...