Data envelopment analysis (DEA) is extended to the case of stochastic inputs and outputs through the use of chance‐constrained programming. The chance‐constrained frontier envelops a given set of observations ‘most of the time’. As an empirical illustration, we re‐examine the pioneering 1981 study of Program Follow Through by Charnes, Cooper and Rhodes.

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Fundamental analysis of stocks links financial data to firm value in two consecutive steps: a predictive information link tying current financial data to future earnings, and a valuation link tying future earnings to firm value. At each step, a large number of causal factors have to be factored into the evaluation. To effect these calculations, we propose a new two-stage multi-criteria procedure, drawing on the techniques of data envelopment analysis. At each stage, a piecewise linear efficiency frontier is fitted to the observed data. The procedure is illustrated by a numerical example, analyzing some 30 stocks in the Spanish manufacturing industry in the years 1991-1996. Copyright © 2004 John Wiley & Sons, Ltd.

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The well-known extremal principle for the solution of spatial equilibrium for a single commodity [Samuelson (1952)] is extended to deal with the case of disequilibrium caused by rigid prices and/or price controls. Both disequilibrium in consumer markets and in factor markets is covered.The study of disequilibrium is well known in general interdependent economic systems (the so-called fixed-price model of interdependent systems). The present work extends this approach to the spatial dimension, and shows how it is possible to represent and solve disequilibrium models by mathematical programming rather than by fixed-point techniques.

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