In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using ...

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using ...

In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecastcombining methodology. When ...

In this study, a methodology to develop hourly demand scenarios in a medium-term horizon for primary distribution substations is presented and applied to a case study. The main contribution of this study is that it addresses ...

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data ...

Buildings are one of the largest consumers of electrical energy, making it important to develop different strategies to help to reduce electricity consumption. Building energy consumption forecasting strategies are widely ...

The development and evaluation of ensemble hindcasting and forecasting systems (EHS and EFS, respectively) for storm surges in the Río de la Plata Estuary (RdP) is presented. The models were forced by atmospheric sea level ...

On behalf of the decision-makers of Andean regulated river basins a drought index was developed to predict the occurrence and extent of drought events. Two stochastic models, the Markov Chain First Order (MCFO) and the ...