By Kenneth Train, Melvyn Weeks (auth.), Riccardo Scarpa, Anna Alberini (eds.)
Simulation equipment are revolutionizing the perform of utilized fiscal research. This quantity collects eighteen chapters written through prime researchers from prestigious learn 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 common source modeling. Empirical instances are used to teach the sensible use and the implications introduced forth by means of different methods.
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Additional info for Applications of Simulation Methods in Environmental and Resource Economics
The mean and standard deviation of the electric vehicle coefﬁcient imply that 94 percent of the population place a negative value of electric vehicles relative to gas. Hybrid vehicles, on the other hand, are preferred to gas vehicles by most consumers, if they were to cost the same. The estimated mean and standard deviation imply that 75 percent have a positive coefﬁcient for the hybrid dummy. Performance is valued at a decreasing rate, as expected. ) The standard deviation of the range coefﬁcient is much lower than of the two performance variables.
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. αn and βn represent the tastes of person n, and these parameters vary over decision-makers because different people have different tastes. In contrast, the scale parameter does not represent a term within the utility function in any given choice situation but rather the standard deviation of utility over different choice situations.
The mixed-logit model is one choice speciﬁcation with this capability which we use as the model for a classical inference. 3. The Mixed Logit Model To illustrate the “mixed logit”5 model we start by assuming that a sampled individual (q = 1, . . , Q) faces a choice among J alternatives in each of T choice situations. Individual q is assumed to consider the full set of offered alternatives in choice situation t and to choose the alternative with the highest utility. 1) where xjtq is the full vector of explanatory variables, including attributes of the alternatives, socio-economic characteristics of the individual and descriptors of the decision context and choice task itself in choice situation t.
Applications of Simulation Methods in Environmental and Resource Economics by Kenneth Train, Melvyn Weeks (auth.), Riccardo Scarpa, Anna Alberini (eds.)