Peer-reviewedLicence not statedJournal of Political Economy Microeconomics
Tuning Parameter Selection in Econometrics
- 1University of California, Los Angeles
Related
- Statistically Valid Post-Training Hyperparameter Selection: From Tuning to Guarantees
- Sensitivity Analysis for Trials with Irregular and Informative Assessment Times: Marginal Mean Models and Sensitivity Parameter Selection
- Can Geopolitical Risk Improve the Forecasting of Thai Stock-Market Returns? Evidence from Econometrics and Machine-Learning Models
- Econometrics with Pre-Trained Embeddings for Unstructured Data
- How to identify factors influencing mode choice behaviors within a heterogeneous metropolitan area? A novel approach midway between data science and econometrics
BibTeX
@article{chetverikov2026tuning,
title = {{Tuning Parameter Selection in Econometrics}},
author = {Denis Chetverikov},
journal = {Journal of Political Economy Microeconomics},
year = {2026},
month = jul,
doi = {10.1086/743398},
url = {http://arxiv.org/abs/2405.03021},
}
RIS
TY - JOUR
TI - Tuning Parameter Selection in Econometrics
AU - Denis Chetverikov
PY - 2026
DA - 2026/07/28
JO - Journal of Political Economy Microeconomics
DO - 10.1086/743398
UR - http://arxiv.org/abs/2405.03021
SN - 2832-9368
SN - 2832-9376
ER -