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Economic Simulation Models in Agricultural Economics: The Current and Possible Future Role of Algebraic Modeling Languages

  • Wolfgang Britz
  • Josef Kallrath
Chapter
Part of the Applied Optimization book series (APOP, volume 104)

Abstract

This contribution is on the current and future role of algebraic modeling languages. While some of the discussion is based on special experience using GAMS for economic simulation models in the field of agricultural economics, other aspects are general to all modeling system, e.g., the modularization of code. Another common aspect is the transition of larger and larger modeling applications and optimization projects into IT projects leading to the open question: How far can we go with algebraic modeling systems?

Keywords

Modeling Language Agricultural Economic Computable General Equilibrium Computable General Equilibrium Model Data Base Management System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Adams, D., Alig, R., McCarl, B.A., Murray, B.C.: FASOMGHG Conceptual Structure, and Specification: Documentation. Tech. rep., Texas A & M University, College Station, Texas (2005)Google Scholar
  2. 2.
    Anderson, P.L.: Business Economics and Finance with Matlab, GIS, and Simulation Models. CRC Press (2004)Google Scholar
  3. 3.
    Banse, M., Grethe, H., Nolte, S., Balkhausen, O.: European Simulation Model (ESIM): Model Documentation. Tech. Rep., University Hohenheim, Hohenheim, Germany (2007)Google Scholar
  4. 4.
    Bauersachs, F.: Ein regional und betriebsgruppenspezifisch differenziertes quantitatives Informationsund Sektoranalysesystem für den Agrarbereich (QUISS): Grundlagen und Modellaufbau. In: Bauersachs, F., Henrichsmeyer, W. (Hrsg.): Beiträge zur quantitativen Sektor- und Regionalanalyse im Agrarbereich, Agrarwirtschaft, Sonderheft 80, S. 5592 (1979)Google Scholar
  5. 5.
    Billups, S.C., Steven, S.P., Ferris, M.C.: A Comparison of Large Scale Mixed Complementarity Problem Solvers. Comput. Optim. Appl. 7, 3–25 (1997)CrossRefGoogle Scholar
  6. 6.
    Bremer, R., Perez, J.S.P., Westfall, P.: Grid computing at texas techuniversity using sas. In: Proceedings of the 14th Annual South- Central SAS Users Group Regional Conference, pp. 64–72. Marquee Associates, LLC, Austian, Texas (2004)Google Scholar
  7. 7.
    Britz, W.: IT—An Unimportant Ingredient of Large Scale Models? Agrarwirtschaft 48, 159–162 (1999)Google Scholar
  8. 8.
    Britz, W.: The Graphical User Interface for CAPRI version 2010. Tech. Rep., Institute for Food and Resource Economics, Chair of Economic and Agricultural Policy, University of Bonn, Bonn, Germany (2010)Google Scholar
  9. 9.
    Britz, W., Heckelei, T.: Recent Developments in EU Policies - Challenges for Partial Equilibrium Models. In: Proceedings of the 107th EAAE Seminar ’Modeling of Agricultural and Rural Development Policies’. January 29th - February 1st, 2008. Sevilla, Spain (2008)Google Scholar
  10. 10.
    Britz, W., Witzke, P.: CAPRI modeling documentation. Tech. Rep., University Bonn, Institute for Food and Resource Economics, Bonn, Germany (2008)Google Scholar
  11. 11.
    Brown, D.: A review of bio-economic models. Tech. Rep., Cornell University African Food Security and Natural Resource Management (CAFSNRM) Program, Ithaca, NY (2000)Google Scholar
  12. 12.
    Chen, C., Mangasarian, O.L.: A class of smoothing functions for nonlinear and mixed complimentarity problems. Comput. Optim. Appl. 5, 97–138 (1996)CrossRefGoogle Scholar
  13. 13.
    Devadoss, S., Westhoff, P.C., Helmar, M.D., Grundmeier, E., Skold, K.D., Meyers, W.H., Johnson, R.S.: The FAPRI Modeling System at CARD: A Documentation Summary, Technical Report 13, December 1989. Tech. Rep., Iowa State University, Food and Agricultural Policy Research Institute, Ames, Iowa (1989)Google Scholar
  14. 14.
    Dol, W.: GAMS Simulation Environment. Tech. Rep., Agricultural Economics Research Institute LEI, The Hague, The Netherlands (2006)Google Scholar
  15. 15.
    Donnellan, T., Hanrahan, K.: Impact analysis of the CAP reform on main agricultural Commodities, EOP report, 15 March 2007. Tech. Rep., Teagasc-Rural Economy Research Centre (RERC) (2007)Google Scholar
  16. 16.
    Drud, A.: On the Use of Second Order Information in GAMS/CONOPT3. In: Proceedings of the SIAM Optimization Meeting, May 2002. SIAM, Toronto (2002)Google Scholar
  17. 17.
    English, B.C., Smith, E.G., Atwood, J.D., Johnson, S.R., Oamek, G.E.: The CARD LP Model: A Documentation Summary, Staff General Research Papers 890. Tech. Rep., Iowa State University, Department of Economics, Ames, Iowa (1993)Google Scholar
  18. 18.
    Gocht, A., Britz, W.: EU-wide Farm Type Supply Models in CAPRI—How to consistently disaggregate Sector Models into Farm Type Models. J. Pol. Model. 33, 146–67 (2010)CrossRefGoogle Scholar
  19. 19.
    Harrison, W., Pearson, K.: Computing Solutions for Large General Equilibrium Models Using GEMPACK. Computational Economics pp. 83–127 (1996)Google Scholar
  20. 20.
    Hazell, P., Norton, R.: Mathematical Programming for Economic Analysis in Agriculture. Macmillan Publishing, New York (1986)Google Scholar
  21. 21.
    Heckelei, T., Britz, W.: Models based on positive Mathematical Programming: State of the Art and Further Expansions. In: Proceedings of the EAAE Symposium Modeling Agricultural Policies: State of the Art and New Challenges, February 3–5, 2005 (2005)Google Scholar
  22. 22.
    Hertel, T. (ed.): Global Trade Analysis: Modeling and Applications. Cambridge University Press, Cambridge (1999)Google Scholar
  23. 23.
    Howitt, R.: Positive Mathematical Programming. Am. J. Agr. Econ. 77, 229–342 (1995)Google Scholar
  24. 24.
    Jame, Y.W., Cutforth, H.W.: Crop growth models for decision support systems. Can. J. Plant Sci. 76, 9–19 (1996)CrossRefGoogle Scholar
  25. 25.
    Leip, A., Marchi, G., Koeble, R., Kempen, M., Britz, W., Li, C.: Linking an economic model for European agriculture with a mechanistic model to estimate nitrogen losses from cropland soil in Europe. Biogeosciences Discuss. 4, 2215–2278 (2007)CrossRefGoogle Scholar
  26. 26.
    Louhichi, K., Janssen, S., Kanellopoulos, A., Li, H., Borkowski, N., Flichman, G., Hengsdijk, H., Zander, P., Blanco, M., Stokstad, G., Athanasiadis, I., Rizzoli, A., Huber, D., Heckelei, T., van Ittersum, M.: Generic Farming System Simulator. In: Brouwer, F.M., Ittersum, M.K. (eds.) Environmental and Agricultural Modeling: Integrated Approaches for Policy Impact Assessment, pp. 109–132. Springer, Dordrecht (2010)CrossRefGoogle Scholar
  27. 27.
    Luna, F., Stefansson, B. (eds.): Economic Simulations in Swarm: Agent-Based Modeling and Object Oriented Programming. Kluwer Academic, Dordrecht and London (2000)Google Scholar
  28. 28.
    McCarl, B.A., Meeraus, A., van der Eijk, P., Bussieck, M., Dirkse, S., Steay, P.: McCarl GAMS User Guide, Version 23.3. Tech. Rep., GAMS Development Corporation, Washington D.C. (2010)Google Scholar
  29. 29.
    Narayanan, B.G., Walmsley, T.L. (eds.): Global Trade, Assistance, and Production: The GTAP 7 Data Base. Center for Global Trade Analysis, Purdue University, West Lafayette, Indiana (2008)Google Scholar
  30. 30.
    OECD: Documentation of the AGLINK-COSIMO Model. OECD/AGR/CA/APM(2006) 16/FINAL. Tech. Rep., OECD, Paris, France (2007)Google Scholar
  31. 31.
    Pearson, K., Horridge, M.: Hands-on Computing with RunGTAP and WinGEM to Introduce GTAP and GEMPACK, GTAP Resource 1683. Tech. Rep., Center for Global Trade Analysis, Purdue University, West Lafayette, Indiana (2003)Google Scholar
  32. 32.
    Renfro, C.G.: A Compendium of Existing Econometric Software Packages. J. Econ. Soc. Meas. 29, 359–409 (2004)Google Scholar
  33. 33.
    Robinson, S., El-Said, M.: GAMS Code for Estimating a Social Accounting Matrix (SAM) using cross entropy methods. TMD discussion paper No. 64. Tech. Rep., International Food Policy Research, Institute, Trade and Macroeconomics Division, Washington DC, USA (2000)Google Scholar
  34. 34.
    Roningen, V.O., Sullivan, J., Dixit, P.: Documentation of the Static World Policy Simulation (SWOPSIM) Modeling Framework. Tech. Rep., ERS, USDA, Washington, DC (1991)Google Scholar
  35. 35.
    Rutherford, T.: Applied General Equilibrium Modeling with MPSGE as a GAMS Subsystem: An Overview of the Modeling Framework and Syntax. Comput. Econ. 14(1–2), 1–46 (1999)CrossRefGoogle Scholar
  36. 36.
    Sharma, G., Martin, J.: MATLAB: A Language for Parallel Computing. Int. J. Parallel Program. 37, 3–36 (2009)CrossRefGoogle Scholar
  37. 37.
    Takayama, T., Judge, G.G.: Spatial and temporal price allocation models. North-Holland Publishing Co., Amsterdam (1971)Google Scholar
  38. 38.
    Velthof, G.L., Oudendag, D., Witzke, H.P., Asman, W.A., Klimont, Z., Oenema, O.: Integrated Assessment of Nitrogen Losses from Agriculture in EU-27 using MITERRA-EUROPE. J. Environ. Qual. 38, 402–417 (2009)CrossRefGoogle Scholar
  39. 39.
    Westhoff, P., Brown, S., Binfield, J.: Why Stochastics Matter: Analyzing Farm and Biofuels Policies. In: Proceedings of the 107th EAAE Seminar ’Modeling of Agricultural and Rural Development Policies’. Sevilla, Spain (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wolfgang Britz
    • 1
  • Josef Kallrath
    • 2
  1. 1.Institute for Food and Resource EconomicsUniversity BonnBonnGermany
  2. 2.Department of AstronomyUniversity of FloridaGainesvilleUS

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