Simulated nonparametric estimation of dynamic models with applications to finance

This paper introduces a new class of parameter estimators for dynamic models, called Simulated Nonparametric Estimators (SNE). The SNE minimizes appropriate distances between nonparametric joint (or conditional) densities estimated from sample data and nonparametric joint (or conditional) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel. This makes the SNE: 1) consistent independently of the amount of smoothing (up to identifiability); and 2) asymptotically root-T normal when the smoothing parameter goes to zero at a reasonably mild rate. Furthermore, the estimator displays the same asymptotic efficiency properties as the maximum-likelihood estimator as soon as the model is Markov in the observable variables. The methods are flexible, simple to implement, and fairly fast; furthermore, they possess finite sample properties that are well approximated by the asymptotic theory. These features are illustrated within the typical estimation problems arising in financial economics.