Inference for low-rank completion without sample splitting with application to treatment effect estimation

Jungjun Choi, Hyukjun Kwon and Yuan Liao

Journal of Econometrics, 2024, vol. 240, issue 1

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.

Keywords: Matrix completion; Nuclear norm penalization; Two-step least squares estimation; Approximate factor model; Causal inference (search for similar items in EconPapers)
JEL-codes: C12 C14 C33 C38 C55 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:240:y:2024:i:1:s0304407624000289

DOI: 10.1016/j.jeconom.2024.105682

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