used a VaR approach to compare these forecasts. With the help of these analyses I examine if combination of the forecast … study first I summarized several specifications for the conditional variance and also define some methods for combination of …

a given tourist destination. We emphasize the presence of quality externalities among hotel establishments, and see that …

series models, both linear and non-linear, have smaller out-of-sample forecast errors than the random walk model. These two …

The aim of this paper is to develop and apply Neural Network (NN) models in order to forecast regional employment … patterns in Germany. NNs are statistical tools based on learning algorithms with a distribution over a large amount of …

This paper examines the forecasting performance of GARCH’s models used with agricultural commodities data. We compare … different possible sources of forecasting improvement, using various statistical distributions and models. We have chosen to … that no real “model leader” was found in this sample of commodities. Finally increased forecast performance is not solely …

ratio when forecasting long-run returns. The empirical results for the S&P 500 show the superiority of our approach to …

because the relationship is unstable over time and, thus, difficult to capture by Granger Causality tests or by forecast …

Rosenstone develops a causal model to forecast political voting. The model seems reasonable; for example, it includes …