  Although using non-Gaussian distributions in economic models has become
increasingly popular, currently there is no systematic way for calibrating a
discrete distribution from the data without imposing parametric assumptions.
This paper proposes a simple nonparametric calibration method based on the
Golub-Welsch algorithm for Gaussian quadrature. Application to an optimal
portfolio problem suggests that assuming Gaussian instead of nonparametric
shocks leads to up to 17% overweighting in the stock portfolio because the
investor underestimates the probability of crashes.
