Youngki Shin
Biographic Data
| ID | 8921098 |
|---|---|
| NAME | Youngki Shin |
| GIVEN NAMES | Youngki |
| FAMILY NAME | Shin |
| SIGNATURE | SHIN Y |
| AFFILIATIONS | Western University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2016 |
| H-INDEX | 0 |
Treatment Effects With Unobserved Heterogeneity
We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments…
Heteroscedastic Transformation Models With Covariate Dependent Censoring
In this article we propose an inferential procedure for transformation models with conditional heteroscedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form. We provide sufficient conditions for point identification. We then propose an estimator and show that it is √n-consistent and asymptotically normal. We conduct a simulation study that reveals adequate finite sample performance. We als…
No prominent works on this page.
Heteroscedastic Transformation Models With Covariate Dependent Censoring
In this article we propose an inferential procedure for transformation models with conditional heteroscedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form. We provide sufficient conditions for point identification. We then propose an estimator and show that it is √n-consistent and asymptotically normal. We conduct a simulation study that reveals adequate finite sample performance. We als…
Treatment Effects With Unobserved Heterogeneity
We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments…
Computer Science (2 works) · Covariate (2 works) · Econometrics (2 works) · Estimator (2 works) · Mathematics (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · Advanced Causal Inference Techniques (1 work) · Average treatment effect (1 work) · Heteroscedasticity (1 work)