"Computing Densities and Expectations in Stochastic Recursive Economies: Generalized Look-Ahead Techniques"

We propose a generalized look-ahead estimator for computing densities and expectations in economic models. We provide conditions under which the estimator converges globally with probability one, and exhibit the asymptotic distribution of the error. Our estimator is more efficient than other Monte Carlo based approaches. Numerical experiments indicate that the estimator can provide large increases in accuracy and speed relative to traditional methods. Particular applications we consider are the stochastic growth model and an income fluctuation problem.