Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.