Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...

This paper presents the concept of Model Validation applied to a Dynamic Stochastic General equilibrium Model (DSGE). The main problem discussed is the approximation of the statistical representation for a DSGE model when not all endogenous variables are observable. MonteCarlo experiments in...

This paper presents the concept of Model Validation applied to a Dynamic Stochastic General equilibrium Model (DSGE). The main problem discussed is the approximation of the statistical representation for a DSGE model when not all endogenous variables are observable. MonteCarlo experiments in...

The paper describes a theoretical approach to determine the downturn LGD for residential mortgages, which is compliant with the regulatory requirement and thus suited to be used for validation, at least as it can give benchmark results. The link between default rates and recovery rates is in...

In recent years state space models, particularly the linear Gaussian version, have become the standard framework for analyzing macro-economic and financial data. However, many theoretically motivated models imply non-linear or non-Gaussian specifications or both. Existing methods for estimating...

We consider an adaptive importance sampling approach to estimating the marginal likelihood, a quantity that is fundamental in Bayesian model comparison and Bayesian model averaging. This approach is motivated by the difficulty of obtaining an accurate estimate through existing algorithms that...

This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time-varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients...

Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian state-space models — where filtering, predictive and smoothing distributions are available in closed form — binary...

Through an estimated and calibrated DSGE model with imperfect competition and nominal rigidities, this work aims to assess the dynamic effects of exogenous perturbations in a small open economy to provide a prescription of a simple monetary policy rule associated with the minimal welfare losses...

This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling...