Semiparametric Estimation of a Dynamic Game of Incomplete Information
Patrick Bajari and
No 320, NBER Technical Working Papers from National Bureau of Economic Research, Inc
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%u2019 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 T1/2 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.
JEL-codes: L0 L5 C1 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-gth and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberte:0320
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in NBER Technical Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().