Abstract
In this chapter, the model presented in Chap. 7 is calibrated and solved with three case studies, which also illustrate the importance of using nonlinear methods. The decision support model would assist utilities in choosing the optimal renewal period for assets.
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Notes
- 1.
An alternative to this approach is to conduct regressions that exclude one variable and then the other to determine if these variables are significant in the absence of the other with which it has a high correlation. If they are not significant in the absence of multicollinearity, then there may be statistical grounds for excluding these variables. If there are no theoretical or statistical grounds for removing any of the variables, then these variables can still be included in the model, because, as identified above, OLS is unbiased by multicollinearity.
- 2.
In the case studies that follow, this violation is addressed in two ways. One can compensate for heteroscedasticity by utilizing White’s covariance matrix in the regression. This technique adjusts the standard error on the coefficient to help determine which variables are in fact significant. Another approach to addressing this violation is to conduct a Robust Least Absolute Error regression. This method is preferred if there are other violations, such as non-normality. However, no goodness of fit statistics are provided with this technique. Therefore, the Robust LAE regression should only be performed if the third violation identified above, non-normality, is present.
- 3.
This was the 10-year bond yield in 2007; but this can be easily varied.
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Dore, M.H. (2015). Computing a Model for Asset Management with Risk. In: Global Drinking Water Management and Conservation. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-319-11032-5_8
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DOI: https://doi.org/10.1007/978-3-319-11032-5_8
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