Identifying Dynamic Discrete Choice Models with Hyperbolic Discounting

TL;DR

This paper demonstrates that key parameters of dynamic discrete choice models with hyperbolic discounting can be uniquely identified from observed data, using variation over time, and presents an effective estimation method validated through simulations.

Contribution

It provides the first identification results for hyperbolic discounting models in finite horizon settings and introduces a practical estimation approach.

Findings

Parameters are point-identified from observed choice and transition data.

The proposed estimator performs well in simulation studies.

Variation in choice probabilities over time is crucial for identification.

Abstract

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.