This paper explores the forecasting abilities of Markov-Switching models. Although MS models generally display a … models. In order to explain this poor performance, we use a forecasting error decomposition. We identify four components and …

produce large reductions in the out-of-sample prediction mean squared error and provides a useful alternative to forecasting …

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 …

forecasting. The parameter space for SSOE models may be specified to match that of the corresponding ARIMA scheme, or it may be … that underlies the Holt-Winters forecasting method. Conditionally heteroscedastic models may be developed in a similar …

to be forecast. The EIC provides a data-driven model selection tool that can be tuned to the particular forecasting task …'s Bayesian Information Criterion (BIC). The comparisons show that for the M3 forecasting competition data, the EIC outperforms …

We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved …

produce large reductions in the out-of-sample prediction mean squared error and provides a useful alternative to forecasting …

This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial … production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate … linear models perform about as well for almost every forecasting horizon …