TY - JOUR
AU - Bajari,Patrick
AU - Hong,Han
TI - Semiparametric Estimation of a Dynamic Game of Incomplete Information
JF - National Bureau of Economic Research Technical Working Paper Series
VL - No. 320
PY - 2006
Y2 - February 2006
DO - 10.3386/t0320
UR - http://www.nber.org/papers/t0320
L1 - http://www.nber.org/papers/t0320.pdf
N1 - Author contact info:
Patrick Bajari
University of Washington
331 Savery Hall
UW Economics Box 353330
Seattle, Washington 98195-3330
E-Mail: Bajari@uw.edu
Han Hong
Stanford University
Landau Economics Building
579 Serra Mall
Stanford, CA 94305
E-Mail: doubleh@stanford.edu
AB - 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.
ER -