Causal Ordering and Beyond
AbstractConstant improvement of computing facilities creates a trend to build always larger macroeconomic models. To master these huge and complex models, much effort must be spent to investigate their functioning, mostly by means of simulations or perturbations of their original structure. Such investigations can be greatly facilitated if one has a sound knowledge of the logical structure of the model. This paper presents a graph theory-based methodology that explores the logical implications of a model's specification. Copyright 1992 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
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Bibliographic InfoArticle provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.
Volume (Year): 33 (1992)
Issue (Month): 4 (November)
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- Faust, Jon & Tryon, Ralph, 1995.
"A Distributed Block Approach to Solving Near-Block-Diagonal Systems with an Application to a Large Macroeconometric Model,"
Society for Computational Economics, vol. 8(4), pages 303-16, November.
- Jon Faust & Ralph Tryon, 1994. "A distributed block approach to solving near-block-diagonal systems with an application to a large macroeconometric model," International Finance Discussion Papers 488, Board of Governors of the Federal Reserve System (U.S.).
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- Hallett, A. J. Hughes & Piscitelli, Laura, 1998. "Simple reordering techniques for expanding the convergence radius of first-order iterative techniques," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1319-1333, August.
- Alessio Moneta, 2003. "Graphical Models for Structural Vector Autoregressions," LEM Papers Series 2003/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Gelman, Irit Askira, 2005. "Addressing time-scale differences among decision-makers through model abstractions," European Journal of Operational Research, Elsevier, vol. 160(2), pages 325-335, January.
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