Semiparametric Estimation of a Dynamic Game of Incomplete Information

Abstract

Recently, empirical industrial organization economists have proposed estimators for dynamic games of incomplete information.In these models, agents choose from a finite number actions and maximize expected discounted utility in a Markov perfect equilibrium.Previous econometric methods estimate the probability distribution of agents' actions in a first stage.In a second step, a finite vector of parameters of the period return function are estimated.In this paper, we develop semiparametric estimators for dynamic games allowing for continuous state variables and a nonparametric first stage.The estimates of the structural parameters are T 1/2 consistent (where T is the sample size) and asymptotically normal even though the first stage is estimated nonparametrically.We also propose sufficient conditions for identification of the model.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 47 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.