A nonparametric approach to solving a simple one-sector stochastic growth model

•We introduce a nonparametric approach to solving nonlinear stochastic dynamic models.•The distinct advantage of this approach is that there are no restrictions placed on the unknown conditional expectations function.•This approach is shown to be stable and accurate when applied to a simple one-sector stochastic growth model.

In this paper we present a nonparametric approach to solving a simple one-sector stochastic growth model. A distinct advantage of our approach is that it does not require placing restrictions on the generally unknown conditional expectations functions. Our method is shown to be accurate and computationally stable when compared to the standard Parameterized Expectations Approach (PEA) and the traditional linear approximation. We demonstrate this using a simple stochastic general equilibrium model with a known solution.