We propose a novel nonparametric minimum-distance estimator for the estimation of simulation models. Our approach leverages a nonparametric smoothing step to approximate the distance between real-world observations and data simulated from a model, allowing for the estimation of model parameters...

We propose a novel nonparametric minimum-distance estimator for the estimation of simulation models. Our approach leverages a nonparametric smoothing step to approximate the distance between real-world observations and data simulated from a model, allowing for the estimation of model parameters...

The paper tackles the issue of possible misspecification in fitting skew normal distributions to empirical data. It is shown, through numerical experiments, that it is easy to choose a distribution which is different from this which actually generated the sample, if the minimum distance...