New models to forecast the real price of oil on the basis of macroeconomic indicators and Google search data are …

at multiple horizons individually, we propose to jointly consider all horizons within a forecast path. We define the …

We propose an automatic machine-learning system to forecast realized volatility for S&P 100 stocks using 118 features … extraordinary performance across forecast horizons, and the improvement in out-of-sample R2's translates into nontrivial economic …

basis of available information at the time the budget is prepared; we find that the forecast error based is six times … greater than the error based on ex-post projection. These results imply that the forecast error predominantly reflects … are even more limited — based only on lagged tax revenues and a GDP growth forecast — provide less-accurate projections …

In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral subcomponents at the … regional level. With an autoregressive distributed lag model we forecast total and sectoral GVA for one German state (Saxony … usage of different forecast pooling strategies and factor models. Our results show that we are able to increase forecast …

models' out-of-sample forecasting performance for the period 2012 q3 to 2016 q2 by using a number of forecast evaluation … Statistics. Additionally, we constructed several composite forecasts in order to test whether a combination forecast is superior … increases. Second, the disaggregated ARIMA model has the smallest forecasting errors. Third, majority of the forecast evaluation …