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Validation of models: Statistical techniques and data availability

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  • Kleijnen, J.P.C.

    (Tilburg University)

Abstract

This paper shows which statistical techniques can be used to validate simulation models, depending on which real-life data are available. Concerning this availability three situations are distinguished (i) no data, (ii) only output data, and (iii) both input and output data. In case (i) - no real data - the analysts can still experiment with the simulation model to obtain simulated data; such an experiment should be guided by the statistical theory on the design of experiments. In case (ii) - only output data - real and simulated output data can be compared through the well-known two-sample Student t statistic or certain other statistics. In case (iii) - input and output data - trace-driven simulation becomes possible, but validation should not proceed in the popular way (make a scatter plot with real and simulated outputs, fit a line, and test whether that line has unit slope and passes through the origin); alternative regression and bootstrap procedures are presented. Several case studies are summarized, to illustrate the three types of situations.

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Bibliographic Info

Paper provided by Tilburg University in its series Open Access publications from Tilburg University with number urn:nbn:nl:ui:12-81791.

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Date of creation: 1999
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Handle: RePEc:ner:tilbur:urn:nbn:nl:ui:12-81791

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Web page: http://www.tilburguniversity.edu/

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  1. Kleijnen, J.P.C. & Pala, O., 1999. "Maximizing the simulation output: A competition," Open Access publications from Tilburg University, Tilburg University urn:nbn:nl:ui:12-80658, Tilburg University.
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Cited by:
  1. Federico Perali & Stefania Lovo, 2009. "Counterfactual analysis using a regional dynamic general equilibrium model with historical calibration," Working Papers, University of Verona, Department of Economics 58/2009, University of Verona, Department of Economics.
  2. Robinson, Stewart, 2002. "General concepts of quality for discrete-event simulation," European Journal of Operational Research, Elsevier, Elsevier, vol. 138(1), pages 103-117, April.
  3. Hertel, Thomas W. & Keeney, Roman & Valenzuela, Ernesto, 2004. "Global Analysis Of Agricultural Trade Liberalization: Assessing Model Validity," 2004 Annual meeting, August 1-4, Denver, CO, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) 20199, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  4. Kleijnen, Jack P.C., 2011. "Ethical issues in engineering models: An operations researcher's reflections," Open Access publications from Tilburg University, Tilburg University urn:nbn:nl:ui:12-4274966, Tilburg University.
  5. Kleijnen, J. P. C., 2001. "Ethical issues in modeling: Some reflections," European Journal of Operational Research, Elsevier, Elsevier, vol. 130(1), pages 223-230, April.
  6. Hertel, Thomas, 2013. "Global Applied General Equilibrium Analysis Using the Global Trade Analysis Project Framework," Handbook of Computable General Equilibrium Modeling, Elsevier, Elsevier.
  7. van der Gaag, Monique A. & Vos, Fred & Saatkamp, Helmut W. & van Boven, Michiel & van Beek, Paul & Huirne, Ruud B. M., 2004. "A state-transition simulation model for the spread of Salmonella in the pork supply chain," European Journal of Operational Research, Elsevier, Elsevier, vol. 156(3), pages 782-798, August.
  8. Happe, Kathrin, 2005. "Agent-Based Modelling and Sensitivity Analysis by Experimental Design and Metamodelling: An Application to Modelling Regional Structural Change," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark, European Association of Agricultural Economists 24464, European Association of Agricultural Economists.

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