Yahong Zhou
Biographic Data
| ID | 6929829 |
|---|---|
| NAME | Yahong Zhou |
| GIVEN NAMES | Yahong |
| FAMILY NAME | Zhou |
| SIGNATURE | ZHOU Y |
| AFFILIATIONS | Chinese Academy of Sciences |
| ORCID | 0000-0002-2482-1976 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Marginal Treatment Effects in the Absence of Instrumental Variables
We propose a method for defining, identifying, and estimating the marginal treatment effect (MTE) without imposing the instrumental variable (IV) assumptions of independence, exclusion, and separability (or monotonicity). Under a new definition of the MTE based on reduced‐form treatment error that is statistically independent of the covariates, we find that the relationship between the MTE and standard treatment parameters holds in the absence of…
High‐Dimensional Oaxaca–Blinder Decomposition With an Application to Gender and Hukou Discrimination in the Chinese Labour Market
High‐dimensional covariates can help justify the unconfoundedness assumption in causal inference and reduce concerns about model misspecification. This paper explores the estimation and inference of counterfactual cumulative distribution functions (CDFs) in a high‐dimensional setting, with a focus on the distributional Oaxaca–Blinder decomposition. We propose two semi‐parametric estimators for the counterfactual CDF, deriving their asymptotic pro…
An Adaptive Kernel-Based Structural Change Test for Copulas
This paper proposes a structural change test for copula models based on the kernel smoothing method. The proposed approach enables adaptable estimation of the dynamic marginal distributions, either parametrically or semi-parametrically. The test statistic is formulated via the weighted quadratic distance between the local smoothing copula and the empirical copula function, utilizing pseudo-observations of marginal distributions. The test statisti…
Axial alignment of covalent organic framework membranes for giant osmotic energy harvesting
Semiparametric Estimation of a Censored Regression Model Subject to Nonparametric Sample Selection
This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulation study indicates that the estimator performs well in various designs and outperforms parametric maximum likelihood estimators. An empirical applicati…
Testing Conditional Mean Independence Under Symmetry
Conditional mean independence (CMI) is one of the most widely used assumptions in the treatment effect literature to achieve model identification. We propose a Kolmogorov–Smirnov-type statistic to test CMI under a specific symmetry condition. We also propose a bootstrap procedure to obtain the p-values and critical values that are required to carry out the test. Results from a simulation study suggest that our test can work very well even in smal…
Root- N Consistent Estimation of a Panel Data Binary Response Model With Unknown Correlated Random Effects
In this article, we consider the estimation of a panel data binary response model with a weak restriction imposed on the individual specific effects. Our estimator is n-consistent and asymptotically normal under reasonable regularity conditions. Furthermore, we allow the error terms to be heteroscedastic over time. The proposed estimator has a closed form expression and thus is very easy to compute. Simulations and the empirical illustration demo…
No prominent works on this page.
Root- N Consistent Estimation of a Panel Data Binary Response Model With Unknown Correlated Random Effects
In this article, we consider the estimation of a panel data binary response model with a weak restriction imposed on the individual specific effects. Our estimator is n-consistent and asymptotically normal under reasonable regularity conditions. Furthermore, we allow the error terms to be heteroscedastic over time. The proposed estimator has a closed form expression and thus is very easy to compute. Simulations and the empirical illustration demo…
Testing Conditional Mean Independence Under Symmetry
Conditional mean independence (CMI) is one of the most widely used assumptions in the treatment effect literature to achieve model identification. We propose a Kolmogorov–Smirnov-type statistic to test CMI under a specific symmetry condition. We also propose a bootstrap procedure to obtain the p-values and critical values that are required to carry out the test. Results from a simulation study suggest that our test can work very well even in smal…
Semiparametric Estimation of a Censored Regression Model Subject to Nonparametric Sample Selection
This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulation study indicates that the estimator performs well in various designs and outperforms parametric maximum likelihood estimators. An empirical applicati…
An Adaptive Kernel-Based Structural Change Test for Copulas
This paper proposes a structural change test for copula models based on the kernel smoothing method. The proposed approach enables adaptable estimation of the dynamic marginal distributions, either parametrically or semi-parametrically. The test statistic is formulated via the weighted quadratic distance between the local smoothing copula and the empirical copula function, utilizing pseudo-observations of marginal distributions. The test statisti…
Axial alignment of covalent organic framework membranes for giant osmotic energy harvesting
Marginal Treatment Effects in the Absence of Instrumental Variables
We propose a method for defining, identifying, and estimating the marginal treatment effect (MTE) without imposing the instrumental variable (IV) assumptions of independence, exclusion, and separability (or monotonicity). Under a new definition of the MTE based on reduced‐form treatment error that is statistically independent of the covariates, we find that the relationship between the MTE and standard treatment parameters holds in the absence of…
High‐Dimensional Oaxaca–Blinder Decomposition With an Application to Gender and Hukou Discrimination in the Chinese Labour Market
High‐dimensional covariates can help justify the unconfoundedness assumption in causal inference and reduce concerns about model misspecification. This paper explores the estimation and inference of counterfactual cumulative distribution functions (CDFs) in a high‐dimensional setting, with a focus on the distributional Oaxaca–Blinder decomposition. We propose two semi‐parametric estimators for the counterfactual CDF, deriving their asymptotic pro…
Mathematics (4 works) · Statistical Methods and Bayesian Inference (4 works) · Statistical Methods and Inference (4 works) · Econometrics (3 works) · Statistics (3 works) · Advanced Causal Inference Techniques (2 works) · Chemistry (2 works) · Conditional independence (2 works) · Economics (2 works) · Artificial Intelligence (1 works)