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I was wondering what is the best way to determine the effect of each random parameter on the result obtained from a Monte Carlo Simulation.

I realise I have asked a similar question here, but this time I am making this question in a more general manner as I didn't get any answers previously.

Background: I'm essentially running a Monte Carlo Simulation where I sample randomly from 65 Normal Distributions and 2 Uniform Distributions. Then, i then input these values to a linear equation and run this for 500.000 iterations. I just wanted to determine the best way to establish the relative effect of each of these 67 (in total) random variables on the final outcome from my Monte Carlo Simulation.

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  • $\begingroup$ Sorry, why use so many iterations? $\endgroup$
    – lbenitesanchez
    Commented Mar 30, 2014 at 10:42
  • $\begingroup$ @lsanchez I needed to have very strict confidence intervals for the data and as I have a high-performance computer at my disposal I thought that there was no harm. $\endgroup$
    – user131983
    Commented Mar 30, 2014 at 10:48

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