Difficult Choices: An Evaluation of Heterogenous Choice Models

While the derivation and estimation of heterogeneous choice models appears straightforward, the properties of such models are not well understood. It is assumed that the properties of these models are identical to those of discrete choice models. We present analytical results that suggest the properties of these models are far more complex. Using a series of Monte Carlo experiments, we further analyze the properties of heteroskedastic probit and heteroskedastic ordered probit models. We test the relative efficiency of these models as well as how robust they are to specification and measurement error. We find that the estimates in heterogeneous choice models tend to be biased in all but ideal conditions and can often lead to incorrect inferences.

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