Using Subjective Expectations Data to Allow for Unobserved Heterogeneity in Hotz-Miller Estimation Strategies

We introduce a novel approach to allow for unobserved heterogeneity in two-step structural estimation strategies for discrete choice dynamic programming models (i.e strategies that avoid full solution methods). We contribute to the literature by adopting a fiÂ…xed effects approach: rather than identifying an unobserved heterogeneity distribution, we actually reveal the true unobserved type of each observation in a first step. We do so by exploiting the tight link between the conditional choice probabilities that are derived from the economic model and just two subjective self-reported assessments about future choice probabilities such as those commonly elicited in major surveys. We uncover the unusual power of ideal expectations data to identify unobserved types for different classes of models. Of more empirical relevance, we show that our results hold when we allow these subjective future choice probabilities to be elicited in less than ideal circumstances, such as, for example, when self-reports display substantial "heaping" at "focal" reference values.