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Deterministic versus Stochastic Sensitivity Analysis in Investment Problems: An Environmental Case Study

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  • Groenendaal, W.J.H. van
  • Kleijnen, J.P.C.

    (Tilburg University, Center for Economic Research)

Abstract

Sensitivity analysis in investment problems is an important tool to determine which factors can jeopardize the future of the investment.Information on the probability distribution of those factors that affect the investment is mostly lacking.In those situations the analysts have two options: (i) apply a method that does not require knowledge of that distribution, or (ii) make assumptions about the distribution.In both approaches sensitivity analysis should result in practical information about the actual importance of potential factors.For approach (i) we apply statistical design of experiments (DOE) in combination with regression analysis or meta-modeling.For approach (ii) we investigate five types of relationships between the model output and each individual factor; Pearson's p, Spearman's rank correlation, and location, dispersion, and statistical dependence.We introduce two distribution types popular with practitioners: uniform and triangular.In an environmental case study both approaches identify the same factors as important.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2001-46.

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Date of creation: 2001
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Handle: RePEc:dgr:kubcen:200146

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Web page: http://center.uvt.nl

Related research

Keywords: sensitivity analysis; experimental design; investment analysis; simulation;

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References

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  1. Groenendaal, W.J.H. van & Kleijnen, J.P.C., 1997. "On the assessment of economic risk: Factorial design versus Monte Carlo methods," Open Access publications from Tilburg University urn:nbn:nl:ui:12-73903, Tilburg University.
  2. Kleijnen, J.P.C. & Bettonvil, B.W.M., 1997. "Searching for important factors in simulation models with many factors: Sequential bifurcation," Open Access publications from Tilburg University urn:nbn:nl:ui:12-73905, Tilburg University.
  3. Frederick S. Hillier, 1963. "The Derivation of Probabilistic Information for the Evaluation of Risky Investments," Management Science, INFORMS, vol. 9(3), pages 443-457, April.
  4. Kleijnen, J.P.C. & Groenendaal, W.J.H. van, 1992. "Simulation: A statistical perspective," Open Access publications from Tilburg University urn:nbn:nl:ui:12-388278, Tilburg University.
  5. Groenendaal, W.J.H. van, 1998. "The Economic Appraisal of Natural Gas Projects," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3804778, Tilburg University.
  6. van Groenendaal, Willem J. H., 1998. "Estimating NPV variability for deterministic models," European Journal of Operational Research, Elsevier, vol. 107(1), pages 202-213, May.
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Cited by:
  1. Borgonovo, E. & Peccati, L., 2011. "Finite change comparative statics for risk-coherent inventories," International Journal of Production Economics, Elsevier, vol. 131(1), pages 52-62, May.
  2. Durieux, Severine & Pierreval, Henri, 2004. "Regression metamodeling for the design of automated manufacturing system composed of parallel machines sharing a material handling resource," International Journal of Production Economics, Elsevier, vol. 89(1), pages 21-30, May.

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