Ziwei Mei
Datos Biográficos
| ID | 8920792 |
|---|---|
| NOMBRE | Ziwei Mei |
| NOMBRES | Ziwei |
| APELLIDO | Mei |
| FIRMA | MEI Z |
| AFILIACIONES | Department of Economics, The Chinese University of Hong Kong |
| ORCID | 0000-0002-6525-9895 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2024 |
| 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…
The boosted Hodrick‐Prescott filter is more general than you might think
The global financial crisis and Covid‐19 recession have renewed discussion concerning trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the popular Hodrick‐Prescott filter to a modern machine learning device suited to data‐rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The …
Sin obras prominentes en esta página.
The boosted Hodrick‐Prescott filter is more general than you might think
The global financial crisis and Covid‐19 recession have renewed discussion concerning trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the popular Hodrick‐Prescott filter to a modern machine learning device suited to data‐rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The …
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 (2 obras) · Financial Risk and Volatility Modeling (2 obras) · Monetary Policy and Economic Impact (2 obras) · Business cycle (1 obras) · Complex Systems and Time Series Analysis (1 obras) · Computer Science (1 obras) · Covariate (1 obras) · Economics (1 obras) · Financial crisis (1 obras) · Gradient boosting (1 obras)