Identifying dynamic discrete choice models off short panels
This paper analyzes the identification of flow payoffs and counterfactual choice probabilities (CCPs) in single-agent dynamic discrete choice models. We develop new results on non-stationary models where the time horizon for the agent extends beyond the length of the data (short panels). We show that counterfactual CCPs in short panels are identified when induced by temporary policy changes affecting payoffs, even though the utility flows are not. Counterfactual CCPs induced by innovations to state transitions are generally not identified unless the model exhibits single action finite dependence, and the payoffs of those actions establishing single action finite dependence are known.
Arcidiacono, P; Miller, RA
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