Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games

We propose a new sequential Efficient Pseudo-Likelihood (EPL) estimator for structural economic models with an equality constraint, particularly dynamic discrete choice games of incomplete information. Each iteration in the EPL sequence is consistent and asymptotically efficient, and iterating to convergence improves finite sample performance. For dynamic single-agent models, we show that Aguirregabiria and Mira's (2002, 2007) Nested Pseudo-Likelihood (NPL) estimator arises as a special case of EPL. In dynamic games, EPL maintains its efficiency properties, although NPL does not. And a convenient change of variable in the equilibrium fixed point equation ensures EPL iterations have the same computational simplicity as NPL iterations. Furthermore, EPL iterations are stable and locally convergent to the finite-sample maximum likelihood estimator at a nearly-quadratic rate for all regular Markov perfect equilibria, including unstable equilibria where NPL encounters convergence problems. Monte Carlo simulations confirm the theoretical results and demonstrate EPL's good performance in finite samples.