Nonparametric Minimum-Distance Estimation of Simulation Models

Abstract

We propose a simulated nonparametric minimum-distance estimator for the estimation of parameters of complex heterogeneous agents models. To address the limitations of traditional simulation-based estimation techniques in cases where the stochastic equicontinuity condition is violated, we approximate the distance between real-world observations and data simulated from a theoretical model using a series of basis functions, allowing for the estimation of model parameters without relying on specific auxiliary models or moment selection. We study the consistency and rates of convergence of our estimator. We investigate its performance through Monte Carlo experiments and an empirical application to financial market data.

Keywords: Simulated minimum-distance, sieve estimation, stochastic equicontinuity