provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly … used to construct a forecast. Second, we discuss random projection regression, where artificial predictors are formed by … randomly weighting the original predictors. Using recent results from random matrix theory, we obtain a tight bound on the mean …

This paper assesses the performance of a number of long-term interest rate forecast approaches, namely time series … is compared using out of sample forecast errors, where a random walk forecast acts as benchmark. It is found that for … five major OECD countries, namely United States, Germany, United Kingdom, The Netherlands and Japan, the other forecasting …

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil … been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights …, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter …

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil … been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights …, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter …

wholesale power markets have only recently been deregulated. We introduce the weather factor into well-known forecasting models … forecasts improves the forecast accuracy, and in particular new models with power transformations of weather forecast variables …

forecasting the term structure of interest rates. As expected, I find that using more flexible models leads to a better in … maturities and forecast horizons. Subsample analysis shows that this outperformance is also consistent over time. …