five major OECD countries, namely United States, Germany, United Kingdom, The Netherlands and Japan, the other forecasting … relevant for forecasting 12 months ahead. …

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related … overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by …

wholesale power markets have only recently been deregulated. We introduce the weather factor into well-known forecasting models …

forecasting range. Next, additional forecasts are computed, by combining the NN methodology with Shift-Share Analysis (SSA). Since …-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators. …

-series techniques to distinct time series of regional data may then become a sub-optimal forecasting strategy. In the field of regional … forecasting of socio-economic variables, both linear and non-linear models have recently been applied and evaluated. However … spatial structure that is required for the estimation of spatial models improves the forecasting performance of non …

practices, forecasting VaR and daily capital charges, and discuss alternative policy recommendations, especially in light of the … to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and … estimated VaR. In this paper we define risk management in terms of choosing sensibly from a variety of risk models, discuss the …

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are … implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework and is applied to … national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 AM, but forecasting results are …