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Found 14 papers, 0 papers with code
Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information
This paper investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available.
Robust Inference in Locally Misspecified Bipartite Networks
Additionally, we introduce bias-aware confidence intervals that account for the effect of the local misspecification.
Adjustment with Many Regressors Under Covariate-Adaptive Randomizations
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.
Covariate Adjustment in Experiments with Matched Pairs
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.
Low-rank Panel Quantile Regression: Estimation and Inference
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.
A Conditional Linear Combination Test with Many Weak Instruments
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.
Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance
We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance.
Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters
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.
Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations
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.
Unconditional Quantile Regression with High Dimensional Data
This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data.
Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs).
Detecting Latent Communities in Network Formation Models
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.
Informational Content of Factor Structures in Simultaneous Binary Response Models
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.
Estimation of Conditional Average Treatment Effects with High-Dimensional Data
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.
