the forecasting process. …

benchmarks in terms of point and density forecasting. The gains are particular high when the full distribution is predicted and …

continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series … contemporary forecasting methods is compared to ours using a number of macroeconomic data. …

distribution theory involves cube-root asymptotics and it is used to shed light on forecasting practice. We show that the … conventional forecasting methods do not necessarily produce the best forecasts in our setting. We also propose a new forecasting … strategy, which incorporates our new distribution theory, and apply our forecasting method to numerous macroeconomic data. The …

Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock … forecasting context. While none of the methods clearly emerges as best, some techniques turn out to be useful to improve the … forecasting performance. …

decomposing, smoothing and forecasting two-dimensional sparse data. In some ways, ROPES is similar to Ridge Regression, the LASSO … practical method of forecasting mortality rates, as well as a new method for interpolating and extrapolating sparse longitudinal …

volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree. …

from the weather forecasting literature known as `ensemble modelling'. In this approach, uncertainty about model … provide two examples of this modelling strategy: (i) forecasting inflation with a disaggregate ensemble; and (ii) forecasting …

We study the performance of Virtual Stock Markets (VSMs) in an institutional forecasting environment. We compare VSMs … of the three approaches differ in forecasting accuracy in a low knowledge-heterogeneity environment. However, where there …