Deterministic versus stochastic sensitivity analysis in investment problems: An environmental case study
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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- 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.
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Other publications TiSEM
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- 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.
- 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.
- 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.
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