This workshop will begin with a series of examples illustrating different settings in which discrete choice models are useful. The multinomial logit model will then be described in detail, followed by extensions to deal with heteroscedastic errors in the utility function, nested choices, and dependent choices. Analysis of various datasets will be illustrated using the mlogit package in R.
