Adam Dearing, Jason R. Blevins
arXiv 22 Dec 2019 · Econometrics · publishedThe Review of Economic Studies (2024) · 3 citations (OpenAlex)
arXiv:1912.10488 · PDF · DOI · OpenAlex · Extracted main text
We propose a new sequential Efficient Pseudo-Likelihood (k-EPL) estimator for dynamic discrete choice games of incomplete information. k-EPL considers the joint behavior of multiple players simultaneously, as opposed to individual responses to other agents' equilibrium play. This, in addition to reframing the problem from conditional choice probability (CCP) space to value function space, yields a computationally tractable, stable, and efficient estimator. We show that each iteration in the k-EPL sequence is consistent and asymptotically efficient, so the first-order asymptotic properties do not vary across iterations. Furthermore, we show the sequence achieves higher-order equivalence to the finite-sample maximum likelihood estimator with iteration and that the sequence of estimators converges almost surely to the maximum likelihood estimator at a nearly-superlinear rate when the data are generated by any regular Markov perfect equilibrium, including equilibria that lead to inconsistency of other sequential estimators. When utility is linear in parameters, k-EPL iterations are computationally simple, only requiring that the researcher solve linear systems of equations to generate pseudo-regressors which are used in a static logit/probit regression. Monte Carlo simulations demonstrate the theoretical results and show k-EPL's good performance in finite samples in both small- and large-scale games, even when the game admits spurious equilibria in addition to one that generated the data. We apply the estimator to study the role of competition in the U.S. wholesale club industry.
appendix boundary found by appendix_command · 68% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Egesdal, M., Z. Lai, and C.-L. Su (2015) Estimating dynamic discrete-choice games of incomplete information | 1.000 | 8 | 3 | 100% |
| 2 | Bugni, F. and J. Bunting (2021) On the iterated estimation of dynamic discrete choice games | 1.000 | 6 | 3 | 100% |
| 3 | Aguirregabiria, V. and P. Mira (2007) Sequential estimation of dynamic discrete games | 0.956 | 32 | 6 | 88% |
| 4 | Aguirregabiria, V. and M. Marcoux (2021) Imposing equilibrium restrictions in the estimation of dynamic discrete games | 0.935 | 11 | 3 | 82% |
| 5 | Kasahara, H. and K. Shimotsu (2008) Pseudo-likelihood estimation and bootstrap inference for structural discrete Markov decision models | 0.928 | 5 | 3 | 80% |
| 6 | Aguirregabiria, V. and P. Mira (2002) Swapping the nested fixed point algorithm: A class of estimators for discrete Markov decision models | 0.874 | 10 | 2 | 100% |
| 7 | Kasahara, H. and K. Shimotsu (2012) Sequential estimation of structural models with a fixed point constraint | 0.794 | 8 | 4 | 50% |
| 8 | Bajari, P., C. Benkard, and J. Levin (2007) Estimating dynamic models of imperfect competition | 0.737 | 3 | 2 | 100% |
| 9 | Rust, J (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher | 0.737 | 3 | 2 | 100% |
| 10 | Pesendorfer, M. and P. Schmidt-Dengler (2010) Sequential estimation of dynamic discrete games: A comment | 0.721 | 8 | 3 | 38% |
Showing the top 10 of 45 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.