-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …

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. …

-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …

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 …

relate to the forecasting environment (which cannot be influenced by the forecasters). Among the variables capturing the … forecasting design (which can, by contrast, be influenced by the forecasters), the size of the dataset from which factors are … unbalanced panel, whether restrictions implied by the factor structure are imposed in the forecasting equation or not and whether …