Nonstationary dynamic models with finite dependence

Abstract

The estimation of nonstationary dynamic discrete choice models typically requires making assumptions far beyond the length of the data. We extend the class of dynamic discrete choice models that require only a few‐period‐ahead conditional choice probabilities, and develop algorithms to calculate the finite dependence paths. We do this both in single agent and games settings, resulting in expressions for the value functions that allow for much weaker assumptions regarding the time horizon and the transitions of the state variables beyond the sample period.