An Efficient Forecasting Procedure for Kuhn-Tucker Consumer Demand Model Systems

This paper proposes an efficient and accurate forecasting algorithm for the MDCEV model. The algorithm builds on simple, yet insightful, analytical explorations with the Kuhn-Tucker conditions of optimality that shed new light on the properties of the MDCEV model. For specific, but reasonably general, functional forms of the consumption utility specification, the algorithm circumvents the need to carry out any iterative constrained optimization procedures that have hitherto been used for forecasting with Kuhn-Tucker (KT) demand model systems. The non-iterative nature of the algorithm contributes significantly to its efficiency and accuracy. Further, although developed in the context of the MDCEV model, the proposed algorithm can be easily modified to be used in the context of other utility maximization-based Kuhn-Tucker (KT) consumer demand model systems in the literature. Simulation experiments highlight the efficiency of the algorithm compared to a traditional iterative forecasting procedure. For example, to forecast the expenditures of 4000 households in 7 expenditure alternatives, for 500 sets of error term draws for each household, the proposed algorithm takes less than 2 minutes. On the other hand, the iterative forecasting routine would take around 2 days to do so.