Cornell University

Joint Econometrics & Industrial Organization Workshop: Federico Bugni

Wednesday, April 18, 2018 at 11:40am to 1:10pm

Uris Hall, 498
Central Campus

Federico Bugni - Duke University

On the Iterated Estimation of Dynamic Discrete Choice Games (joint with J. Bunting)

Abstract We study the asymptotic properties of a class of estimators of the structural parameters in dynamic discrete choice games. We consider K-stage policy iteration (PI) estimators, where K denotes the number of policy iterations employed in the estimation. This class nests several estimators proposed in the literature. By considering a “maximum likelihood” criterion function, our estimator becomes the K-ML estimator in Aguirregabiria and Mira (2002, 2007). By considering a “minimum distance” criterion function, it defines a new K-MD estimator, which is an iterative version of the estimators in Pesendorfer and Schmidt-Dengler (2008) and Pakes et al. (2007).  First, we establish that the K-ML estimator is consistent and asymptotically normal for any K. This complements findings in Aguirregabiria and Mira (2007), who focus on K = 1 and K large enough to induce convergence of the estimator. Furthermore, we show that the asymptotic variance of the K-ML estimator can exhibit arbitrary patterns as a function K.  Second, we establish that the K-MD estimator is consistent and asymptotically normal for any K.  For a specific weight matrix, the K-MD estimator has the same asymptotic distribution as the K-ML estimator. Our main result provides an optimal sequence of weight matrices for the K-MD estimator and shows that the optimally weighted K-MD estimator has an asymptotic distribution that is invariant to K. This new result is especially unexpected given the findings in Aguirregabiria and Mira (2007) for K-ML estimators. Our main result implies two new and important corollaries about the optimal 1-MD estimator (derived by Pesendorfer and Schmidt-Dengler (2008)). First, the optimal 1-MD estimator is optimal in the class of K-MD estimators for all K. In other words, additional policy iterations do not provide asymptotic efficiency gains relative to the optimal 1-MD estimator. Second, the optimal 1-MD estimator is more or equally asymptotically efficient than any K-ML estimator for all K. 

Event Type

Class/ Workshop

Departments

Economics

Tags

EconMetrics, economics, EconSeminar, EconIO

Hashtag

#CornellEcon

Contact E-Mail

alg5@cornell.edu

Contact Name

Amy Moesch

Contact Phone

607-255-5617

Speaker

Federico Bugni

Speaker Affiliation

Duke University

Open To

Cornell Economics Community (List Serve Members)

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