Inference in additively separable models with a high-dimensional set of conditioning variables

Kozbur, Damian

doi:10.1080/07350015.2020.1753524

Inference in additively separable models with a high-dimensional set of conditioning variables

Kozbur, Damian (2021). Inference in additively separable models with a high-dimensional set of conditioning variables. Journal of Business and Economic Statistics, 39(4):984-1000.

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Additional indexing

Item Type:	Journal Article, not_refereed, original work
Communities & Collections:	03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:	330 Economics
Scopus Subject Areas:	Physical Sciences > Statistics and Probability Social Sciences & Humanities > Social Sciences (miscellaneous) Social Sciences & Humanities > Economics and Econometrics Social Sciences & Humanities > Statistics, Probability and Uncertainty
Uncontrolled Keywords:	Statistics, probability and uncertainty, economics and econometrics, social sciences (miscellaneous), statistics and probability, additive nonparametric models, high-dimensional sparse regression, inference under imperfect model selection
Scope:	Discipline-based scholarship (basic research)
Language:	English
Date:	2 October 2021
Deposited On:	11 Feb 2022 12:20
Last Modified:	24 Feb 2025 02:38
Publisher:	American Statistical Association
ISSN:	0735-0015
Additional Information:	Earlier published as ECON Working Paper No. 284: https://www.zora.uzh.ch/id/eprint/151161/
OA Status:	Closed
Publisher DOI:	https://doi.org/10.1080/07350015.2020.1753524
Other Identification Number:	merlin-id:22112
Project Information:	Funder: ETH Postdoctoral Fellowship Grant ID: Project Title: