Estimation and Counterfactuals in Dynamic Structural Models Using an Euler-Equations Policy-Iteration Mapping

The development of two-step econometric methods for dynamic structural models has afforded researchers the ability to estimate models with large state spaces without having to compute a full solution of the model even once. However, regardless of the method used for estimation, the implementation of counterfactual experiments using the estimated model does require the full solution and thus still faces the well-known curse of dimensionality in the solution of dynamic programming models. This paper proposes an approach to compute consistent estimates of counterfactual experiments that avoids the full solution of the model and breaks the curse of dimensionality. We illustrate the computational gains associated with our model and methods using Monte Carlo experiments. Finally we illustrate our method using real data, by studying the effects of a counterfactual increase in the cost of entry in several industries in Chile.