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Teaching Computational Economics in an Applied Economics Program


  • Mario Miranda


Effective teaching of computational methods to economists in an introductory graduate-level course requires difficult choices regarding the material to be covered, the level at which the material will be covered, and the role of assigned exercises, laboratory sessions, and required readings. In this paper, I discuss the goals that I set and the pedagogical choices that I make in teaching computational methods to doctoral students in economics in a quarter-length course. The discussion is based on 15 years of teaching computational methods to students with a broad range of research interests and professional objectives. I also discuss some of the pedagogical obstacles that I often face when teaching the course and how I address them. I hope that the discussion will provide a useful starting point for instructors wishing to develop computational methods courses in other economics graduate programs. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Mario Miranda, 2005. "Teaching Computational Economics in an Applied Economics Program," Computational Economics, Springer;Society for Computational Economics, vol. 25(3), pages 229-254, June.
  • Handle: RePEc:kap:compec:v:25:y:2005:i:3:p:229-254 DOI: 10.1007/s10614-005-2207-x

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    References listed on IDEAS

    1. Stephen J. Turnovsky, 2000. "Methods of Macroeconomic Dynamics, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262201232, January.
    2. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
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    Cited by:

    1. Heshmati, Almas & Lenz-Cesar, Flávio, 2013. "Determinants and Policy Simulation of Firms Cooperation in Innovation," IZA Discussion Papers 7487, Institute for the Study of Labor (IZA).


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