Machine learning and structural econometrics: contrasts and synergies

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Machine learning and structural econometrics : contrasts and synergies. / Iskhakov, Fedor; Rust, John; Schjerning, Bertel.

In: Econometrics Journal, Vol. 23, No. 3, 09.2020, p. S81-S124.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Iskhakov, F, Rust, J & Schjerning, B 2020, 'Machine learning and structural econometrics: contrasts and synergies', Econometrics Journal, vol. 23, no. 3, pp. S81-S124. https://doi.org/10.1093/ectj/utaa019

APA

Iskhakov, F., Rust, J., & Schjerning, B. (2020). Machine learning and structural econometrics: contrasts and synergies. Econometrics Journal, 23(3), S81-S124. https://doi.org/10.1093/ectj/utaa019

Vancouver

Iskhakov F, Rust J, Schjerning B. Machine learning and structural econometrics: contrasts and synergies. Econometrics Journal. 2020 Sep;23(3):S81-S124. https://doi.org/10.1093/ectj/utaa019

Author

Iskhakov, Fedor ; Rust, John ; Schjerning, Bertel. / Machine learning and structural econometrics : contrasts and synergies. In: Econometrics Journal. 2020 ; Vol. 23, No. 3. pp. S81-S124.

Bibtex

@article{e8506be392bd486c88a83f2996f509a7,
title = "Machine learning and structural econometrics: contrasts and synergies",
abstract = "We contrast machine learning (ML) and structural econometrics (SE), focusing on areas where ML can advance the goals of SE. Our views have been informed and inspired by the contributions to this special issue and by papers presented at the second conference on dynamic structural econometrics at the University of Copenhagen in 2018. 'Methodology and Applications of Structural Dynamic Models and Machine Learning'. ML offers a promising class of techniques that can significantly extend the set of questions we can analyse in SE. The scope, relevance and impact of empirical work in SE can be improved by following the lead of ML in questioning and relaxing the assumption of unbounded rationality. For the foreseeable future, however, ML is unlikely to replace the essential role of human creativity and knowledge in model building and inference, particularly with respect to the key goal of SE, counterfactual prediction.",
keywords = "Machine learning, structural econometrics, curse of dimensionality, bounded rationality, counterfactual predictions, DISCRETE-CHOICE MODELS, ECONOMIC-MODELS, EMPIRICAL-MODEL, INFERENCE, CURSE, APPROXIMATION, EQUILIBRIUM, EQUATIONS, DYNAMICS, EARNINGS",
author = "Fedor Iskhakov and John Rust and Bertel Schjerning",
year = "2020",
month = sep,
doi = "10.1093/ectj/utaa019",
language = "English",
volume = "23",
pages = "S81--S124",
journal = "Econometrics Journal",
issn = "1368-4221",
publisher = "Wiley",
number = "3",

}

RIS

TY - JOUR

T1 - Machine learning and structural econometrics

T2 - contrasts and synergies

AU - Iskhakov, Fedor

AU - Rust, John

AU - Schjerning, Bertel

PY - 2020/9

Y1 - 2020/9

N2 - We contrast machine learning (ML) and structural econometrics (SE), focusing on areas where ML can advance the goals of SE. Our views have been informed and inspired by the contributions to this special issue and by papers presented at the second conference on dynamic structural econometrics at the University of Copenhagen in 2018. 'Methodology and Applications of Structural Dynamic Models and Machine Learning'. ML offers a promising class of techniques that can significantly extend the set of questions we can analyse in SE. The scope, relevance and impact of empirical work in SE can be improved by following the lead of ML in questioning and relaxing the assumption of unbounded rationality. For the foreseeable future, however, ML is unlikely to replace the essential role of human creativity and knowledge in model building and inference, particularly with respect to the key goal of SE, counterfactual prediction.

AB - We contrast machine learning (ML) and structural econometrics (SE), focusing on areas where ML can advance the goals of SE. Our views have been informed and inspired by the contributions to this special issue and by papers presented at the second conference on dynamic structural econometrics at the University of Copenhagen in 2018. 'Methodology and Applications of Structural Dynamic Models and Machine Learning'. ML offers a promising class of techniques that can significantly extend the set of questions we can analyse in SE. The scope, relevance and impact of empirical work in SE can be improved by following the lead of ML in questioning and relaxing the assumption of unbounded rationality. For the foreseeable future, however, ML is unlikely to replace the essential role of human creativity and knowledge in model building and inference, particularly with respect to the key goal of SE, counterfactual prediction.

KW - Machine learning

KW - structural econometrics

KW - curse of dimensionality

KW - bounded rationality

KW - counterfactual predictions

KW - DISCRETE-CHOICE MODELS

KW - ECONOMIC-MODELS

KW - EMPIRICAL-MODEL

KW - INFERENCE

KW - CURSE

KW - APPROXIMATION

KW - EQUILIBRIUM

KW - EQUATIONS

KW - DYNAMICS

KW - EARNINGS

U2 - 10.1093/ectj/utaa019

DO - 10.1093/ectj/utaa019

M3 - Journal article

VL - 23

SP - S81-S124

JO - Econometrics Journal

JF - Econometrics Journal

SN - 1368-4221

IS - 3

ER -

ID: 271539111