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 …

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 …

assumed to be Gaussian, the resulting prediction distribution may have an infinite variance beyond a certain forecasting … approximation causes no serious problems for parameter estimation or for forecasting one or two steps ahead. However, for longer …. The performance of the Gaussian approximation is compared with those of two lognormal models for short-term forecasting …

In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based …

. In this paper, we examine how the treatment of prior uncertainty about parameter values can affect forecasting accuracy …