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Teaching Computational Economics to Graduate Students

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  • David Kendrick

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Abstract

The teaching of computational economics to graduate students has mostly been in a single course with a focus on algorithms and computer code. The shortcoming with this approach is that it neglects one of the most important aspects of computational economics - namely model development skills. These skills are the ability to conceptualize the science, engineering and economics of a problem and to convert that understanding first to a mathematical model and then to a computational representation in a software system. Thus we recommend that a two course sequence in computational economics be created for graduate students with the first course focusing on model development skills and the second course on algorithms and the speed and accuracy of computer codes. We believe that a model development course is most helpful to graduate students when it introduces the students to a wide variety of computational models created by past generations and ask them to first make small modification in order to better understand the models, the mathematics and the software. This is turn is followed by encouraging them to make more substantial modifications of the student’s own choosing so as to move the models in directions that permit the students to address current economic problems. We think that the key element of this process is it's enhancement of the creative abilities of our students.

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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 30 (2007)
Issue (Month): 4 (November)
Pages: 381-391

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Handle: RePEc:kap:compec:v:30:y:2007:i:4:p:381-391

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: Graduate teaching; Computational economics; C63; E61;

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  1. Jerome Adda & Russell W. Cooper, 2003. "Dynamic Economics: Quantitative Methods and Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012014, December.
  2. Krusell, P & Smith Jr, A-A, 1995. "Income and Wealth Heterogeneity in the Macroeconomic," RCER Working Papers 399, University of Rochester - Center for Economic Research (RCER).
  3. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, Spring.
  4. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
  5. Hans M. Amman & David A. Kendrick, 1997. "Teaching Macroeconomics with Gams," CARE Working Papers 9702, The University of Texas at Austin, Center for Applied Research in Economics.
  6. Warwick J. McKibbin, 2004. "Climate Change Policy for India," ASARC Working Papers 2004-03, The Australian National University, Australia South Asia Research Centre.
  7. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
  8. Corbae, Dean, 1993. "Relaxing the cash-in-advance constraint at a fixed cost Are simple trigger-target portfolio rules optimal?," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 51-64.
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