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Convex Non-Parametric Least Squares, Causal Structures and Productivity

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Convex Non-Parametric Least Squares, Causal Structures and Productivity. / Tsionas, Mike G.
In: European Journal of Operational Research, Vol. 303, No. 1, 16.11.2022, p. 370-387.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, MG 2022, 'Convex Non-Parametric Least Squares, Causal Structures and Productivity', European Journal of Operational Research, vol. 303, no. 1, pp. 370-387. https://doi.org/10.1016/j.ejor.2022.02.020

APA

Tsionas, M. G. (2022). Convex Non-Parametric Least Squares, Causal Structures and Productivity. European Journal of Operational Research, 303(1), 370-387. https://doi.org/10.1016/j.ejor.2022.02.020

Vancouver

Tsionas MG. Convex Non-Parametric Least Squares, Causal Structures and Productivity. European Journal of Operational Research. 2022 Nov 16;303(1):370-387. Epub 2022 Jun 8. doi: 10.1016/j.ejor.2022.02.020

Author

Tsionas, Mike G. / Convex Non-Parametric Least Squares, Causal Structures and Productivity. In: European Journal of Operational Research. 2022 ; Vol. 303, No. 1. pp. 370-387.

Bibtex

@article{747646b0ce5a4fa2994d0f68970951ef,
title = "Convex Non-Parametric Least Squares, Causal Structures and Productivity",
abstract = "In this paper we consider Convex Nonparametric Least Squares (CNLS) when productivity is introduced. In modern treatments of production function estimation, the issue has gained great importance as when productivity shocks are known to the producers, input choices are endogenous and estimators of production function parameters become inconsistent. As CNLS has excellent properties in terms of approximating arbitrary monotone concave functions, we use it, along with flexible formulations of productivity, to estimate inefficiency and productivity growth in Chilean manufacturing plants. Inefficiency and productivity dynamics are explored in some detail along with marginal effects of contextual variables on productivity growth, inputs, and output. Additionally, we examine the causal structure between inefficiency and productivity as well as model validity based on a causal deconfounding approach. Unlike the Cobb-Douglas and translog production functions, the CNLS system is found to admit a causal interpretation.",
keywords = "Productivity and efficiency, Production functions, Convex non-parametric least squares, Causal models, Deconfounding",
author = "Tsionas, {Mike G.}",
year = "2022",
month = nov,
day = "16",
doi = "10.1016/j.ejor.2022.02.020",
language = "English",
volume = "303",
pages = "370--387",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Convex Non-Parametric Least Squares, Causal Structures and Productivity

AU - Tsionas, Mike G.

PY - 2022/11/16

Y1 - 2022/11/16

N2 - In this paper we consider Convex Nonparametric Least Squares (CNLS) when productivity is introduced. In modern treatments of production function estimation, the issue has gained great importance as when productivity shocks are known to the producers, input choices are endogenous and estimators of production function parameters become inconsistent. As CNLS has excellent properties in terms of approximating arbitrary monotone concave functions, we use it, along with flexible formulations of productivity, to estimate inefficiency and productivity growth in Chilean manufacturing plants. Inefficiency and productivity dynamics are explored in some detail along with marginal effects of contextual variables on productivity growth, inputs, and output. Additionally, we examine the causal structure between inefficiency and productivity as well as model validity based on a causal deconfounding approach. Unlike the Cobb-Douglas and translog production functions, the CNLS system is found to admit a causal interpretation.

AB - In this paper we consider Convex Nonparametric Least Squares (CNLS) when productivity is introduced. In modern treatments of production function estimation, the issue has gained great importance as when productivity shocks are known to the producers, input choices are endogenous and estimators of production function parameters become inconsistent. As CNLS has excellent properties in terms of approximating arbitrary monotone concave functions, we use it, along with flexible formulations of productivity, to estimate inefficiency and productivity growth in Chilean manufacturing plants. Inefficiency and productivity dynamics are explored in some detail along with marginal effects of contextual variables on productivity growth, inputs, and output. Additionally, we examine the causal structure between inefficiency and productivity as well as model validity based on a causal deconfounding approach. Unlike the Cobb-Douglas and translog production functions, the CNLS system is found to admit a causal interpretation.

KW - Productivity and efficiency

KW - Production functions

KW - Convex non-parametric least squares

KW - Causal models

KW - Deconfounding

U2 - 10.1016/j.ejor.2022.02.020

DO - 10.1016/j.ejor.2022.02.020

M3 - Journal article

VL - 303

SP - 370

EP - 387

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -