Standard

Endogeneity in stochastic frontier models. / Amsler, Christine; Prokhorov, Artem; Schmidt, Peter.

In: Journal of Econometrics, Vol. 190, No. 2, 01.02.2016, p. 280-288.

Research output: Contribution to journal › Article › peer-review

Harvard

Amsler, C, Prokhorov, A & Schmidt, P 2016, 'Endogeneity in stochastic frontier models', Journal of Econometrics, vol. 190, no. 2, pp. 280-288. https://doi.org/10.1016/j.jeconom.2015.06.013

APA

Amsler, C., Prokhorov, A., & Schmidt, P. (2016). Endogeneity in stochastic frontier models. Journal of Econometrics, 190(2), 280-288. https://doi.org/10.1016/j.jeconom.2015.06.013

Vancouver

Amsler C, Prokhorov A, Schmidt P. Endogeneity in stochastic frontier models. Journal of Econometrics. 2016 Feb 1;190(2):280-288. https://doi.org/10.1016/j.jeconom.2015.06.013

Author

Amsler, Christine ; Prokhorov, Artem ; Schmidt, Peter. / Endogeneity in stochastic frontier models. In: Journal of Econometrics. 2016 ; Vol. 190, No. 2. pp. 280-288.

BibTeX

@article{194ded2c98f04f0c90cae831365aaf91,
title = "Endogeneity in stochastic frontier models",
abstract = "Stochastic frontier models are typically estimated by maximum likelihood (MLE) or corrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that there is correlation between the inputs and statistical noise or inefficiency. In a standard regression setting, simultaneity is handled by a number of procedures that are numerically or asymptotically equivalent. These include 2SLS; using the residual from the reduced form equations for the endogenous variables as a control function; and MLE of the system that contains the equation of interest plus the unrestricted reduced form equations for the endogenous variables (LIML). We will consider modifications of these standard procedures for the stochastic frontier setting. The paper is mostly a survey and combination of existing results from the stochastic frontier literature and the classic simultaneous equations literature, but it also contains some new results.",
keywords = "Efficiency measurement, Endogeneity, Stochastic frontier",
author = "Christine Amsler and Artem Prokhorov and Peter Schmidt",
year = "2016",
month = feb,
day = "1",
doi = "10.1016/j.jeconom.2015.06.013",
language = "English",
volume = "190",
pages = "280--288",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Endogeneity in stochastic frontier models

AU - Amsler, Christine

AU - Prokhorov, Artem

AU - Schmidt, Peter

PY - 2016/2/1

Y1 - 2016/2/1

N2 - Stochastic frontier models are typically estimated by maximum likelihood (MLE) or corrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that there is correlation between the inputs and statistical noise or inefficiency. In a standard regression setting, simultaneity is handled by a number of procedures that are numerically or asymptotically equivalent. These include 2SLS; using the residual from the reduced form equations for the endogenous variables as a control function; and MLE of the system that contains the equation of interest plus the unrestricted reduced form equations for the endogenous variables (LIML). We will consider modifications of these standard procedures for the stochastic frontier setting. The paper is mostly a survey and combination of existing results from the stochastic frontier literature and the classic simultaneous equations literature, but it also contains some new results.

AB - Stochastic frontier models are typically estimated by maximum likelihood (MLE) or corrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that there is correlation between the inputs and statistical noise or inefficiency. In a standard regression setting, simultaneity is handled by a number of procedures that are numerically or asymptotically equivalent. These include 2SLS; using the residual from the reduced form equations for the endogenous variables as a control function; and MLE of the system that contains the equation of interest plus the unrestricted reduced form equations for the endogenous variables (LIML). We will consider modifications of these standard procedures for the stochastic frontier setting. The paper is mostly a survey and combination of existing results from the stochastic frontier literature and the classic simultaneous equations literature, but it also contains some new results.

KW - Efficiency measurement

KW - Endogeneity

KW - Stochastic frontier

UR - http://www.scopus.com/inward/record.url?scp=84952975395&partnerID=8YFLogxK

U2 - 10.1016/j.jeconom.2015.06.013

DO - 10.1016/j.jeconom.2015.06.013

M3 - Article

AN - SCOPUS:84952975395

VL - 190

SP - 280

EP - 288

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -

ID: 36345855