By Kenneth Train, Melvyn Weeks (auth.), Riccardo Scarpa, Anna Alberini (eds.)
Simulation tools are revolutionizing the perform of utilized monetary research. This quantity collects eighteen chapters written by way of top researchers from prestigious examine associations across the world. the typical denominator of the papers is their relevance for utilized study 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 structures, to evaluate of simulation noise in greatest simulated probability estimates, to dynamic average source modeling. Empirical situations are used to teach the sensible use and the consequences introduced forth by means of the various methods.
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Extra resources for Applications of Simulation Methods in Environmental and Resource Economics
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.
Model in Preference Space with Uncorrelated Coefﬁcients Attribute Price in $10,000’s Operating cost in $/month Range in 100’s of miles Electric engine Hybrid engine High performance Medium or high performance Mini car Small car Large car Small SUV Midsize SUV Large SUV Compact pickup Full-sized pickup Minivan Log likelihood at convergence Parameter Estimate St. ) Mean of coeff. Variance of coeff. Mean of coeff. Variance of coeff. ) Mean of coeff. Variance of coeff. Mean of coeff. Variance of coeff.
2. The Bayesian approach Bayesian methods are often promoted as behaviourally different from, and preferable to classical estimation methods currently used in estimation of advanced discrete choice models such as mixed logit. Brownstone (2001), Chen et al. (2000), Geweke (1999) and Train (2001) provide useful overviews of the Bayesian perspective. Use of information on priors (as structural parameters) and posterior individual-speciﬁc parameter estimates from conditional utility functions are included as information to capture sources of heterogeneity.
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