Beschreibung:
This study explores the ability of a large number of macroeconomic variables to forecast the mean, quantiles and density of key economic indicators. In the baseline case, the forecasts are constructed using an autoregressive model, which assumes that the mean and quantiles depend only on the lagged values of the target variable. We then consider several general specifications that augment the time series model with macroeconomic information, either directly, through factors or forecast combinations. Our findings suggest that aggregating information across quantiles leads to improved estimates of the conditional mean. Overall, augmenting the autoregressive model with macroeconomic variables through methods that perform variable selection or account for non-linearities improves predictive performance