TY - JOUR
T1 - Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action
AU - Bajari, Patrick
AU - Chu, Chenghuan Sean
AU - Nekipelov, Denis
AU - Park, Minjung
N1 - Publisher Copyright:
© 2016, Springer Science+Business Media New York.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - We study identification and estimation of finite-horizon dynamic discrete choice models with a terminal action. We first demonstrate a new set of conditions for the identification of agents’ time preferences. Then we prove conditions under which the per-period utilities are identified for all actions in the agent’s choice-set, without having to normalize the utility for one of the actions. Finally, we develop a computationally tractable semiparametric estimator. The estimator uses a two-step approach that does not use either backward induction or forward simulation. Our methodology can be implemented using standard statistical packages without the need to write specialized computational routines, as it involves linear (or nonlinear) projections only. Monte Carlo studies demonstrate the superior performance of our estimator compared with existing two-step estimation methods. Monte Carlo studies further demonstrate that the ability to identify the per-period utilities for all actions is crucial for counterfactual predictions. As an empirical illustration, we apply the estimator to the optimal default behavior of subprime mortgage borrowers, and the results show that the ability to identify the discount factor, rather than assuming an arbitrary number as typically done in the literature, is also crucial for obtaining correct counterfactual predictions. These findings highlight the empirical relevance of key identification results of the paper.
AB - We study identification and estimation of finite-horizon dynamic discrete choice models with a terminal action. We first demonstrate a new set of conditions for the identification of agents’ time preferences. Then we prove conditions under which the per-period utilities are identified for all actions in the agent’s choice-set, without having to normalize the utility for one of the actions. Finally, we develop a computationally tractable semiparametric estimator. The estimator uses a two-step approach that does not use either backward induction or forward simulation. Our methodology can be implemented using standard statistical packages without the need to write specialized computational routines, as it involves linear (or nonlinear) projections only. Monte Carlo studies demonstrate the superior performance of our estimator compared with existing two-step estimation methods. Monte Carlo studies further demonstrate that the ability to identify the per-period utilities for all actions is crucial for counterfactual predictions. As an empirical illustration, we apply the estimator to the optimal default behavior of subprime mortgage borrowers, and the results show that the ability to identify the discount factor, rather than assuming an arbitrary number as typically done in the literature, is also crucial for obtaining correct counterfactual predictions. These findings highlight the empirical relevance of key identification results of the paper.
KW - Finite horizon optimal stopping problem
KW - Semiparametric estimation
KW - Time preferences
UR - http://www.scopus.com/inward/record.url?scp=85001022649&partnerID=8YFLogxK
U2 - 10.1007/s11129-016-9176-3
DO - 10.1007/s11129-016-9176-3
M3 - Article
AN - SCOPUS:85001022649
SN - 1570-7156
VL - 14
SP - 271
EP - 323
JO - Quantitative Marketing and Economics
JF - Quantitative Marketing and Economics
IS - 4
ER -