Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253612 
Year of Publication: 
2021
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 12 [Issue:] 4 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 1223-1271
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
Imposing equilibrium restrictions provides substantial gains in the estimation of dynamic discrete games. Estimation algorithms imposing these restrictions have different merits and limitations. Algorithms that guarantee local convergence typically require the approximation of high-dimensional Jacobians. Alternatively, the Nested Pseudo-Likelihood (NPL) algorithm is a fixed-point iterative procedure, which avoids the computation of these matrices, but-in games-may fail to converge to the consistent NPL estimator. In order to better capture the effect of iterating the NPL algorithm in finite samples, we study the asymptotic properties of this algorithm for data generating processes that are in a neighborhood of the NPL fixed-point stability threshold. We find that there are always samples for which the algorithm fails to converge, and this introduces a selection bias. We also propose a spectral algorithm to compute the NPL estimator. This algorithm satisfies local convergence and avoids the approximation of Jacobian matrices. We present simulation evidence and an empirical application illustrating our theoretical results and the good properties of the spectral algorithm.
Subjects: 
Dynamic discrete games
nested pseudo-likelihood
fixed-point algorithms
spectral algorithms
convergence
convergence selection bias
JEL: 
C13
C57
C61
C73
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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