  We study identification of dynamic discrete choice models with hyperbolic
discounting. We show that the standard discount factor, present bias factor,
and instantaneous utility functions for the sophisticated agent are
point-identified from observed conditional choice probabilities and transition
probabilities in a finite horizon model. The main idea to achieve
identification is to exploit variation in the observed conditional choice
probabilities over time. We present the estimation method and demonstrate a
good performance of the estimator by simulation.
