Qingliang Fan
Datos Biográficos
| ID | 8879923 |
|---|---|
| NOMBRE | Qingliang Fan |
| NOMBRES | Qingliang |
| APELLIDO | Fan |
| FIRMA | FAN Q |
| AFILIACIONES | Wang Yanan Institute for Studies in Economics (WISE), Department of Statistics, School of Economics and Fujian Key Laboratory of Statistical ScienceXiamen University, Fujian, China() |
| ORCID | 0000-0001-9560-3311 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 4 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 4 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2018 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2025 |
| ÍNDICE H | 0 |
A Heteroscedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size. The test is scale-invariant and robust to heteroskedastic errors. To construct the final test statistic, we first introduce a test based on the maximum norm of multiple parameters that could be high-dimensiona…
Endogenous Treatment Effect Estimation with a Large and Mixed Set of Instruments and Control Variables
Instrumental variables (IVs) and control variables are frequently used to assist researchers in investigating endogenous treatment effects. When used together, their identities are typically assumed to be known. However, in many practical situations, one is faced with a large and mixed set of covariates, some of which can serve as excluded IVs, some can serve as control variables, whereas others should be discarded from the model. It is often not…
Estimation of Conditional Average Treatment Effects With High-Dimensional Data
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. 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. The second stage consists of a low-dimensional local linear regression, reducing CATE …
Nonparametric Additive Instrumental Variable Estimator
In this article, we study a nonparametric approach regarding a general nonlinear reduced form equation to achieve a better approximation of the optimal instrument. Accordingly, we propose the nonparametric additive instrumental variable estimator (NAIVE) with the adaptive group Lasso. We theoretically demonstrate that the proposed estimator is root-n consistent and asymptotically normal. The adaptive group Lasso helps us select the valid instrume…
Sin obras prominentes en esta página.
Nonparametric Additive Instrumental Variable Estimator
In this article, we study a nonparametric approach regarding a general nonlinear reduced form equation to achieve a better approximation of the optimal instrument. Accordingly, we propose the nonparametric additive instrumental variable estimator (NAIVE) with the adaptive group Lasso. We theoretically demonstrate that the proposed estimator is root-n consistent and asymptotically normal. The adaptive group Lasso helps us select the valid instrume…
Estimation of Conditional Average Treatment Effects With High-Dimensional Data
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. 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. The second stage consists of a low-dimensional local linear regression, reducing CATE …
Endogenous Treatment Effect Estimation with a Large and Mixed Set of Instruments and Control Variables
Instrumental variables (IVs) and control variables are frequently used to assist researchers in investigating endogenous treatment effects. When used together, their identities are typically assumed to be known. However, in many practical situations, one is faced with a large and mixed set of covariates, some of which can serve as excluded IVs, some can serve as control variables, whereas others should be discarded from the model. It is often not…
A Heteroscedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size. The test is scale-invariant and robust to heteroskedastic errors. To construct the final test statistic, we first introduce a test based on the maximum norm of multiple parameters that could be high-dimensiona…
Econometrics (4 obras) · Mathematics (4 obras) · Statistics (4 obras) · Computer Science (3 obras) · Covariate (3 obras) · Estimator (3 obras) · Advanced Causal Inference Techniques (2 obras) · Artificial Intelligence (2 obras) · Instrumental variable (2 obras) · Monetary Policy and Economic Impact (2 obras)