JGEA 2017
DOI: 10.21642/jgea.020204af
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R Meets GEMPACK for a Monte Carlo Walk

Abstract: This paper discusses two R functions for reading the output of GEMPACK-based CGE models into R. We highlight the potential of coupling GEMPACK and R by conducting systematic sensitivity analysis of model results using Monte Carlo experiments. We also show how R can enhance the analysis of CGE results by allowing for formal hypothesis testing of the effects of policies which outcome depends on stochastic shocks.JEL codes: C6, C15

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“…Obviously, the LHS approach can be used in other contexts where uncertainty analysis can provide more robust conclusions than a single point estimate. Monte Carlo-type simulations have been used in the context of complex and large CGE models (Villoria (2017) and Villoria and Preckel (2017)) albeit with an analysis of only a small subset of the uncertainty of the parameter space or exogenous shocks. One advantage of the LHS approach over the GQ approach is that the sampling size is less sensitive to the number of uncertain parameters and the desired degree of capturing additional moments of the distribution.…”
Section: Discussionmentioning
confidence: 99%
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“…Obviously, the LHS approach can be used in other contexts where uncertainty analysis can provide more robust conclusions than a single point estimate. Monte Carlo-type simulations have been used in the context of complex and large CGE models (Villoria (2017) and Villoria and Preckel (2017)) albeit with an analysis of only a small subset of the uncertainty of the parameter space or exogenous shocks. One advantage of the LHS approach over the GQ approach is that the sampling size is less sensitive to the number of uncertain parameters and the desired degree of capturing additional moments of the distribution.…”
Section: Discussionmentioning
confidence: 99%
“…The number of required points rises rapidly for higher orders, for example to over 10,000 for 20 uncertain variables and order 4. Villoria and Preckel (2017) argue that using order 3 approximation leaves out significant information on the underlying distribution, for example skewness. The IAM used in this paper has 582 uncertain parameters, which for an order 3 GQ approach would require over 33 million points.…”
Section: Introductionmentioning
confidence: 99%
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