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 | Nonparametric Comparative Statics and Stability |
The authors, leading researchers in the fields of mathematical economics and methodology, present the first comprehensive synthesis of literature on qualitative and other nonparametric techniques, which are important elements of comparative statics and stability analysis in economic theory. The topics covered show how to assess the comparative statics and stability of economic models without a precise quantitative knowledge of all model components. Applications of the analysis range from determining refutable hypotheses from theory to auditing the solutions of large, computer-based systems. This book discusses in depth the methodology involved in a nonparametric analysis of many neoclassical economic models. Constituting a virtually self-contained manual on such analysis, it provides detailed derivation of necessary and sufficient conditions for the existence of restrictive comparative statics and stability results for a range of specified models. Further, algorithms for applying certain of these conditions are given, with examples, as well as the underlying mathematical approach taken. A large body of research is unified covering issues that have been dealt with piecemeal in scattered but important journal articles by the authors and others. The book will prove invaluable to mathematical economists, mathematicians specializing in matrix or graph theory, applied economists working with large-scale economic models, and advanced students of economics. "A valuable and useful resource for researchers on this topic, especially those in operations research, input-output analysis, computable general equilibrium models, and applied linear algebra, as well as for economists and others. The book collects and unifies a large body of research."--Chris Shannon, University of California, Berkeley "This is definitely the last word on these issues. It is extremely thorough and inclusive."--Eugene Silberberg, University of Washington, Seattle Other Princeton books by James Quirk:
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