Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to …

in forecasting from using bivariate models remained small otherwise. …

. While the forecasting error of the combined forecast tends to be systematically smaller than that of the individual model …

We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved …

forecasting financial volatility. We use the auto-covariances of log increments of the multi-fractal process in order to estimate … ?scaling? approach. Our empirical estimates are used in out-of-sample forecasting of volatility for a number of important …

Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of … leads to gains in forecasting accuracy for some time series. …

forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of volatility … leads to gains in forecasting accuracy for some time series. …

macroeconomic policy analysis and forecasting. By means of an empirical application we demonstrate that these models turn out to be …

This paper gives a brief survey of forecasting with panel data. Starting with a simple error component regression and …

represent nicely the two opposing forecasting philosophies. The DSGE model on the one hand has a strong theoretical economic …-horizon forecasting and structural models should be used in long-horizon forecasting. Our paper compareds both state-of-the art data …