Applications of Simulation Methods in Environmental and by Riccardo Scarpa, Anna Alberini

By Riccardo Scarpa, Anna Alberini

Simulation tools are revolutionizing the perform of utilized fiscal research. during this ebook, prime researchers from worldwide speak about interpretation matters, similarities and variations throughout substitute types, and suggest sensible suggestions for the alternative of the version and programming. Case experiences express the sensible use and the consequences introduced forth by means of the several equipment.

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Additional info for Applications of Simulation Methods in Environmental and Resource Economics (The Economics of Non-Market Goods and Resources)

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We compare models that use normal and log-normal distributions for coefficients (called models in preference space) with models using these distributions for WTP (called models in WTP space). We find that the models in preference space fit the data better but provide less reasonable distributions of WTP than the models in WTP space. Our findings suggests that further work is needed to identify distributions that either fit better when applied in WTP space or imply more reasonable distributions of WTP when applied in preference space.

They find that the latter are flexible, perform well, and have a good track record of convergence, especially when the models based on the traditional bivariate approach do not converge easily. 6. Computational aspects Although computational aspects are dealt with throughout the volume, the pedagogical intention of this volume on SMs in environmental and resource economics required that we examine such issues in some detail. Accordingly, the last three chapters deal with these specific aspects of simulation-based methods.

4 2. APPLICATIONS OF SIMULATION METHODS Specification In this section we describe the two types of models. Decision-makers are indexed by n, alternatives by j, and choice situations by t. d. We assume njt is distributed extreme value, though the analysis is the analogous for other distributions. The variance of njt can be different for different decisionmakers: V ar( njt ) = kn2 (π 2 /6), where kn is the scale parameter for decisionmaker n. Though the utility specification is not yet normalized, the current formulation allows us to clarify the circumstances under which the scale parameter can be expected to vary over decision-makers.

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