Abraham Charnes

A Comparison of DEA and Translog Estimates of Production Frontiers Using Simulated Observations from a Known Technology

Data envelopment analysis (DEA), introduced in Charnes, Cooper, and Rhodes (1978), provides a new approach to the estimation of relative efficiencies of decision making units (DMUs). As described by Banker (1980b) and Banker, Charnes, and Cooper (1984), DEA also encompasses estimation of production frontiers making minimal assumptions—such as convexity—about the production possibility set. DEA may be employed to estimate technical and scale efficiencies as in Banker, Charnes, and Cooper, rates of substitution between inputs as in Banker, Charnes, and Cooper and Charnes, Cooper, and Rhodes (1978), and returns to scale and most productive scale sizes as in Banker (1984) and Banker, Charnes, and Cooper (1984). These estimates of different production characteristics pertain to the efficient production surface, unlike the commonly employed regression techniques which estimate the average production correspondence. In this chapter, we report on the results of a simulation study in which DEA was employed to estimate the production frontier from input and output data randomly generated from a known technology.

A Multiperiod Analysis of Market Segments and Brand Efficiency in the Competitive Carbonated Beverage Industry

Measurement and evaluation of sales response, in a multiattribute sense, for a product in the usual marketing environment of competing brands has been and continues to be an exceedingly complex and difficult task. It is made more so by the inability to obtain either comprehensive data or sample data that are free from noise factors, not all of which are recognized a priori or a posteriori. For example, even a casual review of the marketing literature would lead one to conclude that even in a heavily researched area such as the advertising—sales response curve there is little conclusive evidence as to the shape of these curves, and that all such investigations are limited by vitrue of ignoring interactions of marketing mix variables. These studies also, of course, treat only one response variable at a time.