Nonparametric estimation of stochastic frontier models with weak separability

Samuele Centorrino and Christopher F. Parmeter

Journal of Econometrics, 2024, vol. 238, issue 2

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.

Keywords: Stochastic frontier; Weak separability; Monotonicity; Bandwidth selection; Marginal integration (search for similar items in EconPapers)
JEL-codes: C10 C13 C26 C36 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:238:y:2024:i:2:s0304407623003573

DOI: 10.1016/j.jeconom.2023.105641

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