19 citations · 58 across the 15 of their papers we have counts for
17 papers
Adjustment with Many Regressors Under Covariate-Adaptive Randomizations
Liang Jiang, Liyao Li, Ke Miao +1
Our paper discovers a new trade-off of using regression adjustments (RAs) in causal inference under covariate-adaptive randomizations (CARs). On one hand, RAs can improve the effic…
Covariate Adjustment in Experiments with Matched Pairs
Yuehao Bai, Liang Jiang, Joseph P. Romano +2
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 a…
Low-rank Panel Quantile Regression: Estimation and Inference
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. We estimate the hetero…
A Conditional Linear Combination Test with Many Weak Instruments
Dennis Lim, Wenjie Wang, Yichong Zhang
We consider a linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests for inference in IV regressions with…
Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance
Liang Jiang, Oliver B. Linton, Haihan Tang +1
We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-…
Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters
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…
Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations
Liang Jiang, Peter C. B. Phillips, Yubo Tao +1
Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra covariates in addition to the strata indicators. We propose to incorporate these…
Unconditional Quantile Regression with High Dimensional Data
Yuya Sasaki, Takuya Ura, Yichong Zhang
This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel robust score for debiased estimation of the un…
Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs
Liang Jiang, Xiaobin Liu, Peter C. B. Phillips +1
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inf…
Detecting Latent Communities in Network Formation Models
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 e…
Informational Content of Factor Structures in Simultaneous Binary Response Models
Shakeeb Khan, Arnaud Maurel, Yichong Zhang
We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel d…
Estimation of Conditional Average Treatment Effects with High-Dimensional Data
Qingliang Fan, Yu-Chin Hsu, Robert P. Lieli +1
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage,…
Quantile Treatment Effects and Bootstrap Inference under Covariate-Adaptive Randomization
Yichong Zhang, Xin Zheng
In this paper, we study the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose two estimation methods: (1) the simple quan…
Determining the Number of Communities in Degree-corrected Stochastic Block Models
Shujie Ma, Liangjun Su, Yichong Zhang
We propose to estimate the number of communities in degree-corrected stochastic block models based on a pseudo likelihood ratio statistic. To this end, we introduce a method that c…
Strong Consistency of Spectral Clustering for Stochastic Block Models
Liangjun Su, Wuyi Wang, Yichong Zhang
In this paper we prove the strong consistency of several methods based on the spectral clustering techniques that are widely used to study the community detection problem in stocha…
Quasi-Bayesian Inference for Production Frontiers
Xiaobin Liu, Thomas Tao Yang, Yichong Zhang
We propose a quasi-Bayesian method to conduct inference for the production frontier. This approach combines multiple first-stage extreme quantile estimates by the quasi-Bayesian me…
Non-separable Models with High-dimensional Data
Liangjun Su, Takuya Ura, Yichong Zhang
This paper studies non-separable models with a continuous treatment when the dimension of the control variables is high and potentially larger than the effective sample size. We pr…