This paper describes a methodology for the simulation of multivariate out of control situations using in-control data. The method is based on finding the independent factors of the variability of the process, and shifting these factors one by one. These shifts are then translated in terms of the...

Suppose we are interested in forecasting a time series and, in addition to the time series data, we have data from many time series related to the one we want to forecast. Since building a dynamic multivariate model for the set of time series can be a complex task, it is important to measure in...

This paper analyzes the effect of overdifferencing a stationary AR(p+1) process whoselargest root is near unity. It is found that if the process is nearly nonstationary, the estimators ofthe overdifferenced model ARIMA (p, 1, 0) are root-T consistent. It is also found that thismisspecified ARIMA...

This article introduces two new types of prediction errors in time series: the filtered prediction errors and the deletion prediction errors. These two prediction errors are obtained in the same sample used for estimation, but in such a way that they share some common properties with out of...

This article proposes an adaptive forecast combination procedure, denoted as AEC, that tends to be similar to the use of the best available predictor in a time varying environment. In addition, a two-step procedure is proposed to allow the use of alternative combination procedures. In the first...

El cálculo de predicciones puntuales junto con su incertidumbre en forma de intervalo es, en la mayoría de aplicaciones, insuficiente. Especialmente cuando estemos asumiendo no linealidad en los datos, puesto que en estos casos, podrían existir incluso cambios en la distribución. Por ello...