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Train_Discrete Choice Methods with Simulation - Chapter 7

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The discrete choice models that we have discussed – namely, logit, nested logit, probit, and mixed logit – are used in the vast majority of applied work. In the current chapter, we describe several models that are derived under somewhat different behavioral concepts. These data are so called because people reveal their tastes, or preferences, though the choices they make...

Train_Discrete Choice Methods with Simulation - Chapter 8

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The thrust of the wave of discrete choice methods is to free the researcher to specify models that are tailor-made to her situation and issues. n =1 ln P n (β )/N , where P n ( β ) is the probability of the observed outcome for decision maker n, N is the sample size, and β is a K...

Train_Discrete Choice Methods with Simulation - Chapter 9

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In all cases, the researcher wants to calculate an average of the form ¯ t. The moments of the lognormal are functions of the mean and vari- ance of the normal that is exponentiated. Given values for the mean and variance of the lognormal, the appropriate val- ues of b and s to use in the transformation can be calculated....

Train_Discrete Choice Methods with Simulation - Chapter 10

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So far we have examined how to simulate choice probabilities but have not investigated the properties of the parameter estimators that are based on these simulated probabilities. These conditions provide guid- ance to the researcher on how the simulation needs to be performed to obtain desirable properties of the resultant estimator. This procedure is the same as maximum likelihood (ML)...

Train_Discrete Choice Methods with Simulation - Chapter 11

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11.1 Introduction. Figure 11.1 gives the estimated distri- bution of the campground coefficient. For example, the estimates imply that 47 percent of the population dislike having campgrounds at their fishing sites, while the other 53 percent like having them.. Some portion of the population will choose each alternative. They used maximum likelihood procedures to estimate the coefficients for each segment,...

Train_Discrete Choice Methods with Simulation - Chapter 12

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These authors showed how the parameters of the model can be estimated without needing to calculate the choice probabilities. The Bayesian procedures avoid two of the most prominent difficulties associated with classical procedures. With probit and some mixed logit models (especially those with lognormal distributions), maximiza- tion of the simulated likelihood function can be difficult numerically.. Another cost of the...