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Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation

Jungjun Choi, Hyukjun Kwon, Yuan Liao

arXiv 31 Jul 2023 · Econometrics · publishedJournal of Econometrics (2024) · 5 citations (OpenAlex)

arXiv:2307.16370 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

37
references
65
in-text mentions
37
distinct cited
2
self-citations
12,484
main-text words

appendix boundary found by appendix_command · 63% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Chen, Y., Fan, J., Ma, C., and Yan, Y (2019) Inference and uncertainty quantification for noisy matrix completion1.00083100%
2Chernozhukov, V., Hansen, C., Liao, Y., and Zhu, Y (2021) Inference for low-rank models self1.00054100%
3Chernozhukov, V., Hansen, C. B., Liao, Y., and Zhu, Y (2019) Inference for heterogeneous effects using low-rank estimations self0.92844100%
4Chen, Y., Chi, Y., Fan, J., Ma, C., and Yan, Y (2020) Noisy matrix completion: Understanding statistical guarantees for convex relaxation via nonconvex optimization0.81142100%
5Imbens, G. W. and Rubin, D. B (2015) Causal inference in statistics, social, and biomedical sciences0.64422100%
6Jin, S., Miao, K., and Su, L (2021) On factor models with random missing: Em estimation, inference, and cross validation0.64422100%
7Xia, D. and Yuan, M (2021) Statistical inferences of linear forms for noisy matrix completion0.64422100%
8Xiong, R. and Pelger, M (2020) Large dimensional latent factor modeling with missing observations and applications to causal inference. arxiv eprint0.64422100%
9Cox, G. W. and McCubbins, M. D (1986) Electoral politics as a redistributive game0.58531100%
10Larcinese, V., Rizzo, L., and Testa, C (2006) Allocating the us federal budget to the states: The impact of the president0.58531100%

Showing the top 10 of 37 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Inference for Low-rank Models without Estimating the Rank0.92843
20.5cmLow-Rank Estimation of Nonlinear Panel Data Models0.64422
3When can weak latent factors be statistically inferred?0.40511