Andrii Babii
Biographic Data
| ID | 8920496 |
|---|---|
| NAME | Andrii Babii |
| GIVEN NAMES | Andrii |
| FAMILY NAME | Babii |
| SIGNATURE | BABII A |
| AFFILIATIONS | University of North Carolina at Chapel Hill |
| ORCID | 0000-0002-3996-6051 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Panel data nowcasting
The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross‐section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse‐group LASSO regularization which can take advantage of the mixed‐frequency time series panel data structure…
Machine Learning Time Series Regressions With an Application to Nowcasting
This article introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroe…
High-Dimensional Mixed-Frequency IV Regression
No prominent works on this page.
Machine Learning Time Series Regressions With an Application to Nowcasting
This article introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroe…
High-Dimensional Mixed-Frequency IV Regression
Panel data nowcasting
The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross‐section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse‐group LASSO regularization which can take advantage of the mixed‐frequency time series panel data structure…
Computer Science (3 works) · Econometrics (3 works) · Mathematics (3 works) · Monetary Policy and Economic Impact (3 works) · Statistics (3 works) · Artificial Intelligence (2 works) · Estimator (2 works) · Financial Risk and Volatility Modeling (2 works) · Machine learning (2 works) · Nowcasting (2 works)