By Kenneth Train, Melvyn Weeks (auth.), Riccardo Scarpa, Anna Alberini (eds.)
Simulation tools are revolutionizing the perform of utilized financial research. This quantity collects eighteen chapters written through major researchers from prestigious study associations internationally. the typical denominator of the papers is their relevance for utilized learn in environmental and source economics.
The issues 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 structures, to evaluate of simulation noise in greatest simulated chance estimates, to dynamic ordinary source modeling. Empirical situations are used to teach the sensible use and the implications introduced forth by way of the several methods.
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Additional resources for Applications of Simulation Methods in Environmental and Resource Economics
The purpose of the explorations is to focus our attention on the relation between distributions of coefﬁcients and distributions of WTP, rather than to attempt to identify the appropriate distributions to use in all situations. 5. Conclusions This paper examines consumer choice among alternative-fueled vehicles, including gas, electric, and gas/electric hybrids. , have a negative willingness to pay) for electric vehicles relative to gas vehicles, even if operating cost, performance, and range were the same.
Chapter 2 USING CLASSICAL SIMULATION-BASED ESTIMATORS TO ESTIMATE INDIVIDUAL WTP VALUES A Mixed Logit Case Study of Commuters William H. edu David A. au John M. au Abstract A number of papers have recently contrasted classical inference estimation methods for logit models with Bayesian methods. It has been argued that two particularly appealing features of the Bayesian approach are its relative simplicity in estimation, and its ability to derive, individual-speciﬁc willingness to pay (WTP) measures that are less problematic than the classical approaches in terms of extreme values and unexpected signs.
The difference can perhaps be explained by the fact that we used a somewhat 16 APPLICATIONS OF SIMULATION METHODS different method to evaluate out-of-sample ﬁt than they did. We estimated on half the respondents using all of their choice situations and then calculated the log-likelihood for all the choice situations for the other half of the respondents, while they estimated the model on all but one choice situation for each respondent and then calculated the log-likelihood for this one “hold-out” choice situation for each respondent.