Guannan Liu
Biographic Data
| ID | 9678759 |
|---|---|
| NAME | Guannan Liu |
| GIVEN NAMES | Guannan |
| FAMILY NAME | Liu |
| SIGNATURE | LIU G |
| AFFILIATIONS | MOE Key Laboratory of Econometrics, School of Economics and Wang Yanan Institute for Studies in Economics Xiamen University Xiamen China |
| ORCID | 0000-0002-4863-3494 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
A Random Forest–Based Panel Data Approach for Program Evaluation
It is challenging to conduct controlled experiments to assess the impacts of social policy. To address this, past studies propose a panel data approach using factor models to estimate average treatment effects. The selection of control units is a critical step to balance the goodness of fit within‐sample with the posttreatment forecasting error when the number of observed potential control units is large. In this study, we propose using random fo…
Semiparametric estimation and variable selection for single‐index copula models
A copula with a flexibly dependence structure can capture complexity and heterogeneity in economic and financial time series. Based on the recently proposed single‐index copula, we propose a simultaneous variable selection and estimation procedure. This method allows for choosing the most relevant state variables by using a penalized estimation with large sample properties derived. Simulation results demonstrate the good performance of the method…
No prominent works on this page.
Semiparametric estimation and variable selection for single‐index copula models
A copula with a flexibly dependence structure can capture complexity and heterogeneity in economic and financial time series. Based on the recently proposed single‐index copula, we propose a simultaneous variable selection and estimation procedure. This method allows for choosing the most relevant state variables by using a penalized estimation with large sample properties derived. Simulation results demonstrate the good performance of the method…
A Random Forest–Based Panel Data Approach for Program Evaluation
It is challenging to conduct controlled experiments to assess the impacts of social policy. To address this, past studies propose a panel data approach using factor models to estimate average treatment effects. The selection of control units is a critical step to balance the goodness of fit within‐sample with the posttreatment forecasting error when the number of observed potential control units is large. In this study, we propose using random fo…
Computer Science (2 works) · Econometrics (2 works) · Mathematics (2 works) · Statistics (2 works) · Advanced Causal Inference Techniques (1 work) · Artificial Intelligence (1 work) · Evaluation and Performance Assessment (1 work) · Financial Risk and Volatility Modeling (1 work) · Model selection (1 work) · Monetary Policy and Economic Impact (1 work)