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.
I: Econometrics Journal, Bind 23, Nr. 3, 09.2020, s. S81-S124.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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