At the time of publication, A.R. Pinjari was at the University of South Florida, and C. Bhat was at the University of Texas at Austin.This paper proposes simple and computationally efficient forecasting algorithms for a Kuhn-
Tucker (KT) consumer demand model system called the
Multiple Discrete-Continuous Extreme
Value (MDCEV) model. The algorithms build on simple, yet insightful, analytical explorations
with the Kuhn-Tucker conditions of optimality that
shed new light on the properties of the
model. Although developed for the MDCEV model, the
proposed algorithm can be easily
modified to be used for other KT demand model systems in the literature with additively
separable utility functions. The MDCEV model and the forecasting algorithms proposed in this
paper are applied to a household-level energy consumption dataset to analyze residential energy
consumption patterns in the United States. Further,
simulation experiments are undertaken to
assess the computational performance of the propose
d (and existing) KT demand forecasting
algorithms for a range of choice situations with small and large choice sets.Civil, Architectural, and Environmental Engineerin