, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting …

using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear …

-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of … Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield … competitive with the other forecasting models considered. …

selection of forecasting models to tackle their common data features yet distinct problems. …

We propose a simple and reproducible methodology to create a single equation forecasting model (SEFM) for low …-frequency macroeconomic variables. Our methodology is illustrated by forecasting annual real GDP growth rates for 52 African countries, where … methodology quickly arrives at a reasonably small forecasting model per country. Compared with benchmark models, the single …

We investigate the marginal predictive content of small versus large jump variation, when forecasting one …

volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate …

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the … simulation experiment. The results show that the trade-off for selecting variables in forecasting models in a stationary world …

improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques …-horizon Superior Predictive Ability (uSPA) tests, used to select the best forecasting model by combining different horizons. Our sample … forecasting horizons that are more than one month ahead using the mean square error, and the aggregated ETS has better forecasting …