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Deterministic versus stochastic sensitivity analysis in investment problems: An environmental case study

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  • Van Groenendaal, Willem J. H.
  • Kleijnen, Jack P. C.
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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Suggested Citation

  • Van Groenendaal, Willem J. H. & Kleijnen, Jack P. C., 2002. "Deterministic versus stochastic sensitivity analysis in investment problems: An environmental case study," European Journal of Operational Research, Elsevier, vol. 141(1), pages 8-20, August.
    • Handle: RePEc:eee:ejores:v:141:y:2002:i:1:p:8-20
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    1. 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.
    2. 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.
    3. van Groenendaal, W.J.H., 1998. "The Economic Appraisal of Natural Gas Projects," Other publications TiSEM a0ff517c-2041-4457-adac-7, Tilburg University, School of Economics and Management.
    4. Kleijnen, J.P.C. & Bettonvil, B.W.M., 1997. "Searching for important factors in simulation models with many factors : Sequential bifurcation," Other publications TiSEM be826993-22f9-4cb3-89df-3, Tilburg University, School of Economics and Management.
    5. van Groenendaal, W.J.H. & Kleijnen, J.P.C., 1997. "On the assessment of economic risk : Factorial design versus Monte Carlo methods," Other publications TiSEM fd2a2307-0812-4543-8151-7, Tilburg University, School of Economics and Management.
    6. Bettonvil, Bert & Kleijnen, Jack P. C., 1997. "Searching for important factors in simulation models with many factors: Sequential bifurcation," European Journal of Operational Research, Elsevier, vol. 96(1), pages 180-194, January.
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    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.
    3. Sener Salci & Glenn P. Jenkins, 2016. "Incorporating Risk and Uncertainty in Cost-Benefit Analysis," Development Discussion Papers 2016-09, JDI Executive Programs.
    4. Borgonovo, E. & Gatti, S. & Peccati, L., 2010. "What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions," European Journal of Operational Research, Elsevier, vol. 205(1), pages 227-236, August.
    5. Rabitti, Giovanni & Borgonovo, Emanuele, 2020. "Is mortality or interest rate the most important risk in annuity models? A comparison of sensitivity analysis methods," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 48-58.
    6. Mike Hewitt & Janosch Ortmann & Walter Rei, 2022. "Decision-based scenario clustering for decision-making under uncertainty," Annals of Operations Research, Springer, vol. 315(2), pages 747-771, August.
    7. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    8. Happe, Kathrin & Kellermann, Konrad & Balmann, Alfons, 2006. "Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 11(1).

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