Nonparametric estimation of stochastic frontier models with weak separability

Abstract

We propose a robust and versatile approach to estimate the stochastic frontier model which avoids parametric assumptions. Our approach requires a single continuous covariate which monotonically influences the conditional mean of inefficiency. Subject to these conditions, the frontier and the conditional mean of inefficiency can be estimated nonparametrically. The estimator we propose uses local least squares and marginal integration making it easy to implement across statistical software. A range of Monte Carlo simulations suggests that when our main identification condition holds, our proposed estimator outperforms other proposals that currently exist. Finally, we provide an application to the study of undercounting COVID-19 cases across the United States. Whereas our method indicates significant undercounting, consistent with existing evidence, other nonparametric methods suggest far less undercounting.