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 model is chosen. The results show that a sharp...

This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by extension, forecasting depends on numerical methods for simulating from the posterior distribution of the parameters and special attention is given to the implementation of the...

I use Bayesian VARs to forecast global temperatures anomalies until the end of the XXI century by exploiting their cointegration with the Joint Radiative Forcing (JRF) of the drivers of climate change. Under a ‘no change’ scenario, the most favorable median forecast predicts the land...