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 by way of major researchers from prestigious examine associations internationally. the typical denominator of the papers is their relevance for utilized learn in environmental and source economics.
The subject matters variety 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 usual source modeling. Empirical circumstances are used to teach the sensible use and the consequences introduced forth through the several methods.
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Additional info for Applications of Simulation Methods in Environmental and Resource Economics
5 It is also referred to in various literatures as random parameter logit (RPL), mixed multinomial logit (MMNL), kernel logit, hybrid logit and error components logit.
Our aim is not to estimate Bayesian contrasts per se but to show that the classical inference approach is likewise straightforward – indeed the individualspeciﬁc estimates are a by-product of the parameter estimation process. We also reveal the beneﬁts of calculating WTP measures from ratios of individual param- 18 APPLICATIONS OF SIMULATION METHODS eters which are behaviourally more appealing approximations to the true values of each individual, in contrast to draws from population distributions that run the risk of allocating two parameters that are poorly juxtaposed in a relative sense, resulting in extreme value estimates.
5 Their 6 John GAUSS code is made available upon request. McPeak also uses code for GAUSS and he makes it available upon request. K. 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.