Imposing equilibrium restrictions in the estimation of dynamic discrete games
| Autor: | Victor Aguirregabiria, Mathieu Marcoux |
|---|---|
| Přispěvatelé: | Université de Montréal. Faculté des arts et des sciences. Département de sciences économiques |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Dynamic discrete games
Economics and Econometrics Computer science nested pseudo-likelihood media_common.quotation_subject Computation Stability (learning theory) fixed-point algorithms symbols.namesake C73 Dynamic discrete game 0502 economics and business Convergence (routing) ddc:330 Nested pseudo-likelihood C13 C57 Applied mathematics spectral algorithms 050207 economics 050205 econometrics media_common Estimation Selection bias convergence 05 social sciences Estimator Fixed point algorithms Local convergence Convergence selection bias convergence selection bias C61 Jacobian matrix and determinant symbols Convergence |
| Popis: | 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. Dynamic discrete games nested pseudo‐likelihood fixed‐point algorithms spectral algorithms convergence convergence selection bias C13 C57 C61 C73 |
| Databáze: | OpenAIRE |
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