Imposing equilibrium restrictions in the estimation of dynamic discrete games

Imposing equilibrium restrictions provides substantial gains in the estimation of dynamic discrete games. Estimation algorithms imposing these have different merits and limitations. Algorithms that guarantee local convergence typically require approximation high‐dimensional Jacobians. Alternatively, Nested Pseudo‐Likelihood (NPL) algorithm is a fixed‐point iterative procedure, which avoids computation matrices, but—in games—may fail to converge consistent NPL estimator. In order better capture effect iterating finite samples, we study asymptotic properties this for data generating processes are neighborhood stability threshold. We find there always samples fails converge, introduces selection bias. also propose spectral compute This satisfies Jacobian matrices. present simulation evidence an empirical application illustrating our theoretical results good algorithm.