arXiv 4 Aug 2023 · Statistics — Methodology · publishedJournal of the American Statistical Association (2024) · 6 citations (OpenAlex)
arXiv:2308.02364 · PDF · DOI · OpenAlex · Extracted main text
This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the number of missing entries is small enough compared to the panel size, then they can be estimated well even when missing is not at random. Taking advantage of this fact, we divide the missing entries into smaller groups and estimate each group via nuclear norm regularization. In addition, we show that with appropriate debiasing, our proposed estimate is asymptotically normal even for fairly weak signals. Our work is motivated by recent research on the Tick Size Pilot Program, an experiment conducted by the Security and Exchange Commission (SEC) to evaluate the impact of widening the tick size on the market quality of stocks from 2016 to 2018. While previous studies were based on traditional regression or difference-in-difference methods by assuming that the treatment effect is invariant with respect to time and unit, our analyses suggest significant heterogeneity across units and intriguing dynamics over time during the pilot program.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Agarwal, A., Dahleh, M., Shah, D., and Shen, D (2021) Causal matrix completion | 1.000 | 9 | 3 | 100% |
| 2 | Bai, J. and Ng, S (2021) Matrix completion, counterfactuals, and factor analysis of missing data | 1.000 | 7 | 4 | 100% |
| 3 | Athey, S., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K (2021) Matrix completion methods for causal panel data models | 0.969 | 11 | 4 | 91% |
| 4 | Chung, K. H., Lee, A. J., and Rösch, D (2020) Tick size, liquidity for small and large orders, and price informativeness: Evidence from the tick size pilot program | 0.909 | 12 | 3 | 75% |
| 5 | Agarwal, A., Shah, D., and Shen, D (2020) Synthetic interventions | 0.874 | 5 | 2 | 100% |
| 6 | Koltchinskii, V., Lounici, K., Tsybakov, A. B., et al (2011) Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion | 0.737 | 5 | 3 | 40% |
| 7 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program | 0.737 | 3 | 2 | 100% |
| 8 | Chernozhukov, V., Hansen, C., Liao, Y., and Zhu, Y (2021) Inference for low-rank models | 0.737 | 3 | 2 | 100% |
| 9 | Albuquerque, R., Song, S., and Yao, C (2020) The price effects of liquidity shocks: A study of the sec’s tick size experiment | 0.644 | 2 | 2 | 100% |
| 10 | Werner, I. M., Rindi, B., Buti, S., and Wen, Y (2022) Tick size, trading strategies, and market quality | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2401.13665 | 1.000 | 6 | 3 |
| 2 | Robust Matrix Estimation with Side Information | 0.843 | 4 | 3 |
| 3 | Causal Forecasting in Panel Data: A Two-Way Synthetic Forecasting Approach | 0.644 | 2 | 2 |
| 4 | Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models | 0.585 | 3 | 1 |
| 5 | Large-dimensional Factor Analysis with Weighted PCA | 0.405 | 1 | 1 |