This paper describes the development of algorithms to solve large-scale macroeconometric models. The bulk of this paper was written in 1970 when the problem of finding solutions for these large-scale models was still an issue. During the 1960s, large powerful (for that time period) computers became available for use by economists who proceeded to use this powerful new tool to develop and apply econometric software to the problem of estimating large-scale macroeconometric models such as the Wharton Quarterly Model and the Brookings Model. Having succeeded in estimating these models, the next problem was to find a way to solve them either in the context of simulation experiments or forecasting exercises. During the late 1960s, the author investigated the problem of solving these large-scale models and found several acceptable methods which are presented in this paper.

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This article summarizes much of the literature on price discrimination, discussing the alterative approaches and the general conclusions on price discrimination's impact on output, prices, and welfare. It applies lessons from the literature to a specific proposal that would eliminate arbitrage in California wholesales gasoline markets, and in effect prevent some forms of price discrimination. It concludes that applying extensions of the existing models to industry data is very important for gaining a better understanding of the implications price discrimination, and the current debate on price dispersion would benefit from such research.Price Discrimination, Uniform Pricing, Gasoline, Oligopolistic Competition, Ricardian Equilibrium,

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