A0476
Title: Identification of dynamic discrete choice models with hyperbolic discounting using a terminating action
Authors: Chao Wang - Dongbei University of Finance and Economics (China) [presenting]
Ruli Xiao - Indiana University (United States)
Stefan Weiergraeber - Keystone AI (United States)
Abstract: The identification of dynamic discrete choice models with hyperbolic discounting is studied using a terminating action. Novel identification results are provided for both sophisticated and naive agents' discount factors and their utilities in a finite horizon framework under the assumption of a stationary flow utility. In contrast to existing identification strategies, it is not required to observe the final period for the sophisticated agent. Moreover, it is avoided to normalize the flow utility of a reference action for both the sophisticated and the naive agent. Two simple estimators are proposed: One that estimates the two discount factors without specifying the flow utilities and another that jointly estimates both the discount factors and the flow utilities. It is shown that both estimators perform well in simulations.
