1 citations · 1 across the 12 of their papers we have counts for
12 papers
Specification Testing for Dyadic Regression Models
Ulrich Hounyo, Jiahao Lin, Xiaojun Song
This paper develops omnibus specification tests for linear conditional-mean models with undirected dyadic data. We establish a uniform projection theorem that reduces the dyadic pr…
Paired Sample Tests for High-dimensional Uncorrelatedness via Random Integration
Shiyao Huang, Xiaojun Song
This paper proposes a novel nonparametric test to assess the uncorrelatedness between two high-dimensional random vectors. We develop our test by generalizing the random integratio…
Robust Inference for Dyadic Data with Spatially Dependent Nodes
Ulrich Hounyo, Jiahao Lin, Xiaojun Song
We develop inference for complete dyadic samples with spatial dependence governed by geographic distances between nodes, covering settings beyond the scope of existing dyadic infer…
Testing Heteroskedasticity Under Measurement Error
Xiaojun Song, Jichao Yuan
In this paper, we propose a novel approach to detect heteroskedasticity in regression models with regressors contaminated by measurement error. Specifically, inspired by the integr…
Asymmetric Huber Periodogram
Tianbo Chen, Xiaojun Song
This paper introduces a novel spectral M-estimator, called the asymmetric Huber periodogram (AHP), as a generalization of the ordinary periodogram (PG), the quantile periodogram (Q…
Testing linearity in semi-functional partially linear regression models
Yongzhen Feng, Jie Li, Xiaojun Song
This paper proposes a Kolmogorov-Smirnov type statistic and a Cramér-von Mises type statistic to test linearity in semi-functional partially linear regression models. Our test stat…
Unified Inference on Moment Restrictions with Nuisance Parameters
Xingyu Li, Xiaojun Song, Zhenting Sun
This paper proposes a simple unified inference approach on moment restrictions in the presence of nuisance parameters. The proposed test is constructed based on a new characterizat…
Nonparametric Tests of Conditional Independence for Time Series
Xiaojun Song, Haoyu Wei
We propose consistent nonparametric tests of conditional independence for time series data. Our methods are motivated from the difference between joint conditional cumulative distr…
Data-driven Smooth Tests for Normality in ANOVA When the Number of Groups is Large
Peiwen Jia, Xiaojun Song, Haoyu Wei
The normality assumption for random errors is fundamental in the analysis of variance (ANOVA) models. However, it is rarely subjected to formal testing in practice, and theoretical…
Specification tests for generalized propensity scores using double projections
Pedro H. C. Sant'Anna, Xiaojun Song
This paper proposes a new class of nonparametric tests for the correct specification of models based on conditional moment restrictions, paying particular attention to generalized…
Covariate Distribution Balance via Propensity Scores
Pedro H. C. Sant'Anna, Xiaojun Song, Qi Xu
This paper proposes new estimators for the propensity score that aim to maximize the covariate distribution balance among different treatment groups. Heuristically, our proposed pr…
Specification Tests for the Propensity Score
Pedro H. C. Sant'Anna, Xiaojun Song
This paper proposes new nonparametric diagnostic tools to assess the asymptotic validity of different treatment effects estimators that rely on the correct specification of the pro…