We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...

We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across...

Based on the approach advanced by Elliott et al. (Rev. Ec. Studies. 72, 1197-1125,2005), we analyzed whether the loss function of a sample of exchange rate forecasters is asymmetric in the forecast error. Using forecasts of the euro/dollar exchange rate, we found that the shape of the loss...

Using forecasts of the Brazilian real and the Mexican peso, we analyze the shape of the loss function of exchange-rate forecasters and the rationality of their forecasts. We find a substantial degree of cross-sectional heterogeneity with respect to the shape of the loss function. While some...

We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...

Based on the approach advanced by Elliott et al. (Rev. Ec. Studies. 72, 1197..1125, 2005), we analyzed whether the loss function of a sample of exchange rate forecasters is asymmetric in the forecast error. Using forecasts of the euro/dollar exchange rate, we found that the shape of the loss...

Using forecasts of the Brazilian real and the Mexican peso, we analyze the shape of the loss function of exchange-rate forecasters and the rationality of their forecasts. We find a substantial degree of cross-sectional heterogeneity with respect to the shape of the loss function. While some...