Showing papers in "International Journal of Forecasting in 2006"

TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.

TL;DR: A review of the past 25 years of research into time series forecasting can be found in this paper, where the authors highlight results published in journals managed by the International Institute of Forecasters.

TL;DR: Gardner as discussed by the authors reviewed the research in exponential smoothing since the original work by Brown and Holt and brought the state-of-the-art up to date by introducing a new class of state-space models with a single source of error.

TL;DR: The main models of innovation diffusion were established by 1970 as discussed by the authors, and the main categories of these modifications are: the introduction of marketing variables in the parameterisation of the models; generalising the models to consider innovations at different stages of diffusions in different countries; and generalizing the models by considering the diffusion of successive generations of technology.

TL;DR: In this article, the authors compared the performance of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead and concluded that simpler and more robust methods can outperform more complex alternatives.

TL;DR: The past 25 years has seen a phenomenal growth of interest in judgemental approaches to forecasting and a significant change of attitude on the part of researchers to the role of judgement as discussed by the authors.

TL;DR: Approaches and developments in demographic and population forecasting since 1980 are reviewed, with a focus on the approaches to population forecasting, demographic process forecasting and error estimation.

TL;DR: In this paper, a threshold heteroscedastic model which integrates threshold nonlinearity and GARCH-type conditional variance for modeling mean and volatility asymmetries in financial markets is proposed.

TL;DR: A statistical forecasting system for the short-term prediction (up to 48 h ahead) of the wind energy production of a wind farm using an on-line adaptive forecast combination scheme to obtain the final prediction.

TL;DR: The results indicate that the information criterion approach appears to provide the best basis for an automated approach to method selection, provided that it is based on Akaike's information criterion.

TL;DR: In this article, the authors proposed a model that accommodates autoregression and weekly seasonals in both the conditional mean and conditional volatility of returns, as well as leverage effects via an EGARCH specification.

TL;DR: This paper found that most of sentiment measures are caused by returns and volatility rather than vice versa, and that lagged returns cause volatility, while sentiment measures have extremely limited forecasting power once returns are included as a forecasting variable.

TL;DR: In this paper, the authors summarize the progress made over the past quarter century with respect to methods for reducing forecasting error and provide evidence on the best forecasting procedures to use under given conditions.

TL;DR: A systematic algorithm which tests for the existence of collective self-organization in the behavior of agents in social systems, with a concrete empirical implementation on the Dow Jones Industrial Average over the 20th century and on the Hong Kong Hang Seng composite index since 1969, exhibits a remarkable ability for generalization.

TL;DR: In this article, the authors developed time varying parameter (TVP) linear almost ideal demand system (LAIDS) models in both long-run (LR) static and short-run error correction (EC) forms.

TL;DR: In this article, the authors evaluate the relative merits of three alternative approaches to extracting information from a large data set for forecasting, namely, the use of an automated model selection procedure, the adoption of a factor model, and the usage of single-indicator-based forecast pooling.

TL;DR: In this paper, the authors analyzed the out-of-sample forecasting performance of a number of prominent nonlinear models of U.S. dollar real exchange rate behavior from the extant empirical literature.

TL;DR: In this article, a stochastic model which employs generalized long memory (by means of Gegenbauer processes) to model the seasonal behavior of the load was proposed to avoid modeling the intricate intra-day pattern (load profile) displayed by the load, which varies throughout the week as well as through the seasons.

TL;DR: In this paper, a method for generating coherent, integer out-of-sample predictions is proposed and used in the context of the data, and the results of the exercise are central in allowing for parameter uncertainty in the forecast distributions.

TL;DR: In this paper, the authors used 1- to 10-day-ahead temperature ensemble predictions to forecast the mean and quantiles of the density of the payoff from a 10 day heating degree day put option.

TL;DR: Based on a panel of German professional forecasts for 1970-2004, the authors analyzes the dispersion of growth and inflation forecasts and finds that forecast dispersion varies over time and is particularly high during recessions.

TL;DR: A stronger emphasis on the method of multiple hypotheses and on invited replications of important research is recommended, which will help to bridge the gap between theory and practice.

TL;DR: In this paper, the authors proposed a point and volatility forecasting method for the imbalance volume in the transmission market, which is defined as the sum of all actions taken to balance the system.

TL;DR: It is concluded that the forecasting journals have covered all areas of forecasting research; however, many influential articles are published across a wide range of other journals, and there was little cross-fertilisation between journals.

TL;DR: An overview of the history of forecasting software over the past 25 years is presented, concentrating especially on the interaction between computing and software.

TL;DR: In this article, a Granger Causality test for the U.S. civilian unemployment rate is proposed, and it is shown that real investment, real GDP and real interest rate are helpful for improving the in-sample fit of unemployment.

TL;DR: In this article, an extension of existing multivariate Markov-switching models is proposed for analyzing current business conditions and making predictions about the future state of the Euro-area economy in real time.

TL;DR: In the early 1940s, the Cowles Commission for Research as discussed by the authors fostered the development of statistical methodology for application in economics and paved the way for large-scale econometric models to be used for both structural estimation and forecasting.

TL;DR: The empirical performance of various models in assessing the effects of policy variables, legal changes, and traffic security campaigns are compared and it is concluded that forecast combinations based on disaggregated models display better performance.

TL;DR: In this article, the authors present Markov chain Monte Carlo and importance sampling techniques for volatility estimation, model misspecification testing and comparisons for general volatility models, including GARCH and stochastic volatility formulations.