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...

We study the asymptotic properties of a class of estimators of the structural parameters in dynamic discrete choice games. We consider K-stage policy iteration (PI) estimators, where K denotes the number of policy iterations employed in the estimation. This class nests several estimators...

Single‐agent dynamic discrete choice models are typically estimated using heavily parametrized econometric frameworks, making them susceptible to model misspecification. This paper investigates how misspecification affects the results of inference in these models. Specifically, we consider a...

We introduce evolutionary dynamics for two-action games where agents with diverse preferences use statistical inference to guide their behavior. In each period, agents are randomly selected to revise actions. They draw a random sample of other agents’ actions, use statistical inference to...

This paper proposes approximate variational inference methods for estimation of a strategic model of social interactions. Players interact in an exogenous network and sequentially choose a binary action. The utility of an action is a function of the choices of neighbors in the network. I prove...

I present a peer effects model for count data using a static game of incomplete information. I provide sufficient conditions under which the game equilibrium is unique. I estimate the model's parameters using the Nested Partial Likelihood approach and establish asymptotic properties of the...

Concern over the distributional effects of policies which induce changes in peer group structure, or associational redistributions (Durlauf, 1996c), motivates a substantial body of theoretical and empirical research in economics, sociology, psychology, and education. A growing collection of...

We propose the relaxation algorithm as a simple and powerful method for simulating the transition process in growth models. This method has a number of important advantages: (1) It can easily deal with a wide range of dynamic systems including stiff differential equations and systems giving rise...

We propose the relaxation algorithm as a simple and powerful method for simulating the transition process in growth models. This method has a number of important advantages: (1) It can easily deal with a wide range of dynamic systems including multi-dimensional systems with stable eigenvalues...

We propose the relaxation algorithm as a simple and powerful method for simulating the transition process in growth models. This method has a number of important advantages: (1) It can easily deal with a wide range of dynamic systems including multi-dimensional systems with stable eigenvalues...