bank's own interest rate expectations. From a theoretical point of view, the latter should yield the highest forecast …

This paper considers forecast averaging when the same model is used but estimation is carried out over different … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks … estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or …

. Unbiasedness depends on the forecast horizon being analyzed, with longer-term four-quarter-ahead forecasts being biased. I …

distribution of the forecast errors made by the institutes, and then fit a skewed t-distribution to the estimated quantiles. We use … the resulting density forecasts to compute the log probability score of the predicted forecast errors. Based on an …

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in …

extent they adjust an econometric model-based forecast. In this paper we show, while making just two simple assumptions, that … econometric model. A key feature of the data that facilitates our estimates is that we have forecast updates for the same forecast …