Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Estimation of semi- and nonparametric stochastic frontier models with endogenous regressors. / Prokhorov, Artem; Tran, Kien C.; Tsionas, Mike G.
в: Empirical Economics, Том 60, № 6, 24.09.2020, стр. 3043-3068.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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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 -
ID: 85598496