By Kenneth Train, Melvyn Weeks (auth.), Riccardo Scarpa, Anna Alberini (eds.)
Simulation tools are revolutionizing the perform of utilized fiscal research. This quantity collects eighteen chapters written by way of major researchers from prestigious learn associations across the world. the typical denominator of the papers is their relevance for utilized examine in environmental and source economics.
The subject matters 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 overview of simulation noise in greatest simulated probability estimates, to dynamic ordinary source modeling. Empirical circumstances are used to teach the sensible use and the consequences introduced forth by way of different methods.
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Additional info for Applications of Simulation Methods in Environmental and Resource Economics
Uk Abstract In models with unobserved taste heterogeneity, distributional assumptions can be placed in two ways: (1) by specifying the distribution of coefﬁcients in the utility function and deriving the distribution of willingness to pay (WTP), or (2) by specifying the distribution of WTP and deriving the distribution of coefﬁcients. In general the two approaches are equivalent, in that any mutually compatible distributions for coefﬁcients and WTP can be represented in either way. However, in practice, convenient distributions, such as normal or log-normal, are usually speciﬁed, and these convenient distributions have different implications when placed on WTP’s than on coefﬁcients.
3 Correlated coefﬁcients and WTP In general, neither coefﬁcients nor WTP’s are independent. We estimated a model in preference space with correlated coefﬁcients and a model in WTP space with correlated WTP’s. The model in preference space incorporates random scale, since it allows correlation between all coefﬁcients. The two models (in preference space and WTP space) are therefore the same in allowing for random scale and differ only in the distributional assumptions for coefﬁcients and WTP. Both models assume a log-normal price coefﬁcient.
For example, uncorrelated preference coefﬁcients translate into WTP’s that are correlated in a particular way that would be hard to implement and test in the context of WTP distributions, and vice-versa. ” For the models in preference space, a convenient distribution is speciﬁed for the coefﬁcients, and the parameters of this distribution (such as its mean and variance) are estimated. The distribution of WTP’s is then derived from the estimated distribution of coefﬁcients. This is currently the standard practice for application of choice models.