TY - JOUR

T1 - Estimation of semi- and nonparametric stochastic frontier models with endogenous regressors

AU - Prokhorov, Artem

AU - Tran, Kien C.

AU - Tsionas, Mike G.

N1 - Prokhorov, A., Tran, K.C. & Tsionas, M.G. Estimation of semi- and nonparametric stochastic frontier models with endogenous regressors. Empir Econ 60, 3043–3068 (2021). https://doi.org/10.1007/s00181-020-01941-0

PY - 2020/9/24

Y1 - 2020/9/24

N2 - This paper considers the problem of estimating a nonparametric stochastic frontier model with shape restrictions and when some or all regressors are endogenous. We discuss three estimation strategies based on constructing a likelihood with unknown components. One approach is a three-step constrained semiparametric limited information maximum likelihood, where the first two steps provide local polynomial estimators of the reduced form and frontier equation. This approach imposes the shape restrictions on the frontier equation explicitly. As an alternative, we consider a local limited information maximum likelihood, where we replace the constrained estimation from the first approach with a kernel-based method. This means the shape constraints are satisfied locally by construction. Finally, we consider a smooth-coefficient stochastic frontier model, for which we propose a two-step estimation procedure based on local GMM and MLE. Our Monte Carlo simulations demonstrate attractive finite sample properties of all the proposed estimators. An empirical application to the US banking sector illustrates empirical relevance of these methods.

AB - This paper considers the problem of estimating a nonparametric stochastic frontier model with shape restrictions and when some or all regressors are endogenous. We discuss three estimation strategies based on constructing a likelihood with unknown components. One approach is a three-step constrained semiparametric limited information maximum likelihood, where the first two steps provide local polynomial estimators of the reduced form and frontier equation. This approach imposes the shape restrictions on the frontier equation explicitly. As an alternative, we consider a local limited information maximum likelihood, where we replace the constrained estimation from the first approach with a kernel-based method. This means the shape constraints are satisfied locally by construction. Finally, we consider a smooth-coefficient stochastic frontier model, for which we propose a two-step estimation procedure based on local GMM and MLE. Our Monte Carlo simulations demonstrate attractive finite sample properties of all the proposed estimators. An empirical application to the US banking sector illustrates empirical relevance of these methods.

KW - Constrained semiparametric limited information MLE

KW - Efficiency

KW - Endogeneity

KW - Local limited information MLE

KW - Smooth coefficient

KW - Stochastic frontier

KW - INEFFICIENCY

KW - LEAST-SQUARES

KW - KERNEL REGRESSION

KW - GMM ESTIMATION

KW - PANEL-DATA

KW - VARIABLES

KW - TECHNOLOGY

UR - http://www.scopus.com/inward/record.url?scp=85091686949&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/93d35dce-6d86-3598-8e8c-2319c0465976/

U2 - 10.1007/s00181-020-01941-0

DO - 10.1007/s00181-020-01941-0

M3 - Article

AN - SCOPUS:85091686949

VL - 60

SP - 3043

EP - 3068

JO - Empirical Economics

JF - Empirical Economics

SN - 0377-7332

IS - 6

ER -