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 … squared forecast error for both randomized methods. We identify settings in which one randomized method results in more …

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 … approaches do not outperform the random walk, or a somewhat more sophisticated time series model, on a 3 month forecast horizon …

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil …, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter …-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach …

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil …, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter …-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach …

In this paper we consider regression models with forecast feedback. Agents' expectations are formed via the recursive …

) model with those for ARIMA(1,d,1) models withfixed order of d=0 and d=1 for inflation. Comparing meansquared forecast errors …,1,1) model provides the best forecasts, but itsmulti-step forecast intervals are too large. …