Journal article
Inference for low-rank completion without sample splitting with application to treatment effect estimation
Journal of econometrics, Vol.240(1), pp.105682-23
03/01/2024
DOI: 10.1016/j.jeconom.2024.105682
Abstract
This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that the least square estimation of eigenvectors following the nuclear norm penalization attains the asymptotic normality. The key contribution of our method is that it does not require sample splitting. In addition, this paper allows dependent observation patterns and heterogeneous observation probabilities. Empirically, we apply the proposed procedure to estimating the impact of the presidential vote on allocating the U.S. federal budget to the states.
Details
- Title: Subtitle
- Inference for low-rank completion without sample splitting with application to treatment effect estimation
- Creators
- Jungjun Choi - Columbia UniversityHyukjun Kwon - Rutgers, The State University of New JerseyYuan Liao - Rutgers, The State University of New Jersey
- Resource Type
- Journal article
- Publication Details
- Journal of econometrics, Vol.240(1), pp.105682-23
- DOI
- 10.1016/j.jeconom.2024.105682
- ISSN
- 0304-4076
- eISSN
- 1872-6895
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 03/01/2024
- Academic Unit
- Economics
- Record Identifier
- 9984936839902771
Metrics
1 Record Views