Download Applications of Simulation Methods in Environmental and by Kenneth Train, Melvyn Weeks (auth.), Riccardo Scarpa, Anna PDF

By Kenneth Train, Melvyn Weeks (auth.), Riccardo Scarpa, Anna Alberini (eds.)

Simulation equipment are revolutionizing the perform of utilized monetary research. This quantity collects eighteen chapters written via top researchers from prestigious examine associations internationally. the typical denominator of the papers is their relevance for utilized examine in environmental and source economics.

The issues diversity from discrete selection modeling with heterogeneity of personal tastes, to Bayesian estimation, to Monte Carlo experiments, to structural estimation of Kuhn-Tucker call for platforms, to evaluate of simulation noise in greatest simulated chance estimates, to dynamic usual source modeling. Empirical situations are used to teach the sensible use and the consequences introduced forth through the several methods.

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Extra info for Applications of Simulation Methods in Environmental and Resource Economics

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Sonnier et al. (2003) estimated models in preference space and WTP space, using the terms “linear models” and “nonlinear models” instead of our terminology to denote that the random customer-level parameters enter utility linearly in the former and nonlinearly in the later. Their results are consistent with our main conclusions, in that they obtained better within-sample fit for their model in preference space but more reasonable WTP distributions for their model in WTP space. However, their results differ from ours in one regard.

Models in which the response to the initial and follow-up payment question are assumed to be driven by two different-and unobserved-WTP amounts. The two latent WTP amounts are allowed to be correlated. ), and (iii) a model that truncates the bivariate distribution to allow for the incentives to be different for individuals who answer no to the initial payment question, and individuals who answer yes to the initial payment question. , a joint distribution for two variables with specified marginals.

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. A random scale parameter is conceptually different from random values for α and β.

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