We employ data-driven models to generate probabilistic forecasts of the wind using vector auto-regression (VAR) and time-of-day forecasts. Bayesian optimization is employed to find the optimum of an ...

In this context, we propose a new forecasting method called Incremental Learning Vector Auto Regression (ILVAR). It works by minimizing the variance difference between actual and forecasted values as ...

This paper constructs a financial conditions index for Poland to explore the link between financial conditions and real economic activity. The index in constructed by applying two complementary ...

This paper shows that vector auto regression (VAR) with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results of De Mol and co-workers (2008) and show that, when ...

This paper presents a novel approach to detail the propagation of shocks to public debt. The modeling technique involves a structural vector auto-regression (SVAR) estimator with an endogenous debt ...

We propose a classical approach to estimate factor-augmented vector auto-regressive (FAVAR) models with time variation in the parameters.