Energy system models are limited in their scope and level of disaggregation by the availability offast computing hardware. While improvements in hardware and solver developments have led toan increasing size of solvable models, problems with high temporal and geographical resolutionremain...

Purpose: This research aims to develop a dynamic and self-regulated application that considers demand forecasts, based on linear regression as a basic algorithm for machine learning. Methodology: This research uses aggregate planning and machine learning along with inventory policies through the...

I show that sharp identified sets in a large class of econometric models can be characterized by solving linear systems of equations. These linear systems determine whether, for a given value of a parameter of interest, there exists an admissible joint distribution of unobservables that can...