Working Paper: NBER ID: w21125
Authors: Patrick Bajari; Victor Chernozhukov; Han Hong; Denis Nekipelov
Abstract: In this paper, we study the identification and estimation of a dynamic discrete game allowing for discrete or continuous state variables. We first provide a general nonparametric identification result under the imposition of an exclusion restriction on agent payoffs. Next we analyze large sample statistical properties of nonparametric and semiparametric estimators for the econometric dynamic game model. We also show how to achieve semiparametric efficiency of dynamic discrete choice models using a sieve based conditional moment framework. Numerical simulations are used to demonstrate the finite sample properties of the dynamic game estimators. An empirical application to the dynamic demand of the potato chip market shows that this technique can provide a useful tool to distinguish long term demand from short term demand by heterogeneous consumers.
Keywords: dynamic discrete games; semiparametric estimation; nonparametric identification; consumer demand
JEL Codes: C01; C14; C57; C73; L0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Identification framework allows for nonparametric identification of dynamic discrete games under exclusion restrictions (C73) | Identification of payoffs for agents (C78) |
| Identification framework allows for nonparametric identification of dynamic discrete games under exclusion restrictions (C73) | Distinguishing long-term demand from short-term demand in the context of heterogeneous consumers in the potato chip market (D12) |
| Semiparametric efficiency of dynamic discrete choice models can be achieved using a sieve-based conditional moment framework (C51) | Achievement of semiparametric efficiency (C14) |
| Estimation procedure reduces to one stage (C51) | Full nonparametric estimation of player payoffs without needing to solve for equilibria (C72) |
| Numerical simulation demonstrates finite sample properties of estimators (C51) | Demonstration of finite sample properties (C59) |