This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short-term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of...

Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses...

This article provides a discussion of Clements and Galvão’s “Forecasting with Vector Autoregressive Models of Data Vintages: US output growth and inflation.” Clements and Galvão argue that a multiple-vintage VAR model can be useful for forecasting data that are subject to revisions....

This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving...

This paper examines whether deviations from PPP are stationary in the presence of nonlinearity, and whether the adjustment toward PPP is symmetric from above and below. Using alternative nonlinear models, our results support mean reversion and asymmetric adjustment dynamics. We find differences...

Using monthly data for a set of variables, we examine the out-of-sample performance of various variance/covariance models and find that no model has consistently outperformed the others. We also show that it is possible to increase the probability mass toward the tails and to match reasonably...

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential...

This paper introduces a time-varying threshold autoregressive model (TVTAR), which is used to examine the persistence of deviations from PPP. We find support for the stationary TVTAR against the unit root hypothesis; however, for some developing countries, we do not reject the TVTAR with a unit...

The most common forecasting methods in business are based on exponential smoothing and the most common time series in business are inherently non-negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to...

Exponential smoothing (ES) with ARCH (autoregressive conditionally heteroscedastic) and GARCH (generalized ARCH) errors are introduced. This is done for a large class of ES methods, those for which the forecasts are obtained using a set of additive updating formulas, and also those for which an...