no code implementations • 22 Mar 2024 • Shujie Ma, Po-Yao Niu, Yichong Zhang, Yinchu Zhu
This paper investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available.
no code implementations • 20 Mar 2024 • Luis E. Candelaria, Yichong Zhang
Additionally, we introduce bias-aware confidence intervals that account for the effect of the local misspecification.
no code implementations • 17 Apr 2023 • Liang Jiang, Liyao Li, Ke Miao, Yichong Zhang
On the other hand, RAs can degrade estimation efficiency due to their estimation errors, which are not asymptotically negligible when the number of regressors is of the same order as the sample size.
no code implementations • 9 Feb 2023 • Yuehao Bai, Liang Jiang, Joseph P. Romano, Azeem M. Shaikh, Yichong Zhang
This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to "matched pairs" and it is additionally desired to adjust for observed, baseline covariates to gain further precision.
no code implementations • 20 Oct 2022 • Yiren Wang, Liangjun Su, Yichong Zhang
In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time.
no code implementations • 22 Jul 2022 • Dennis Lim, Wenjie Wang, Yichong Zhang
Under strong identification, our linear combination test has optimal power against local alternatives among the class of invariant or unbiased tests which are constructed based on jackknife AR and LM tests.
no code implementations • 31 Jan 2022 • Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang
We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance.
no code implementations • 31 Aug 2021 • Wenjie Wang, Yichong Zhang
We study the wild bootstrap inference for instrumental variable regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity.
no code implementations • 31 May 2021 • Liang Jiang, Peter C. B. Phillips, Yubo Tao, Yichong Zhang
We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs.
no code implementations • 27 Jul 2020 • Yuya Sasaki, Takuya Ura, Yichong Zhang
This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data.
no code implementations • 25 May 2020 • Liang Jiang, Xiaobin Liu, Peter C. B. Phillips, Yichong Zhang
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs).
no code implementations • 7 May 2020 • Shujie Ma, Liangjun Su, Yichong Zhang
This paper proposes a logistic undirected network formation model which allows for assortative matching on observed individual characteristics and the presence of edge-wise fixed effects.
no code implementations • 3 Oct 2019 • Shakeeb Khan, Arnaud Maurel, Yichong Zhang
Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models.
no code implementations • 6 Aug 2019 • Qingliang Fan, Yu-Chin Hsu, Robert P. Lieli, Yichong Zhang
In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size.