Before revision:  RNNs do not assume a functional form, can learn nonconvex combinations of control units, and are specifically structured to exploit temporal dependencies in sequential data. I apply the approach to the problem of estimating the long-run impacts of U.S. homestead policy on public school spending.
After revision:  RNNs do not assume a functional form, can learn nonconvex combinations of control units, and are specifically structured to exploit temporal dependencies in panel data, and are able to learn negative and nonlinear interactions between control unit outcomes. We apply the method to the problem of estimating the long-run impacts of U.S. homestead policy on public school spending.