Three essays on divisia decomposition and discrete choice model with nested structure

Abstract

In the first essay we present a method for decomposing industrial energy demand using the Divisia approach. This method involves decomposition of the aggregate energy-intensity index measured in terms of energy consumption per unit of output. The factors considered are changes in production structure and sectoral energy intensities. The effect associated with sectoral energy intensities is further decomposed into those associated with fuel substitution and real energy intensities. We have applied the methodology to data of the Korean manufacturing industry. The results obtained show that increases in aggregate intensity for total energy consumption since 1988 are due primarily to the effect of increased real energy intensities. The contributions from structural changes and interfuel substitution were relatively small. In the second essay we present the distribution of the maximum random utility in the GEV model and that of the maximum random utilities within the separable subsets in the GEV model with an additively separable generator function. We then formulate the inclusive value of the entire choice set in the GEV model and that of a separable subset of alternatives in the GEV model and that of a separable subset of alternatives in the GEV model with an additively separable generator function and relate them to the consumer``s surplus. In the third essay we present two closely related asymptotic discrete choice models modelling choices among groups of alternatives: Gumbel grouped utility model in the utility maximization context and Weibull grouped cost model in the cost minimization context. They both based on limiting distribution of extreme values and their difference arises from the tail property of the utility or cost distribution in the direction of choice(bounded for cost minimization context and unbounded in the utility maximization context). We apply the result to consumer search model.