Jonas Striaukas
Biographic Data
| ID | 8921526 |
|---|---|
| NAME | Jonas Striaukas |
| GIVEN NAMES | Jonas |
| FAMILY NAME | Striaukas |
| SIGNATURE | STRIAUKAS J |
| AFFILIATIONS | LIDAM UC Louvain and Research Fellow of the Fonds de la Recherche Scientifique—FNRS, Brussels, Belgium |
| ORCID | 0000-0001-7658-8638 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Factor-Augmented Sparse Midas Regressions with an Application to Nowcasting
This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our novel approach integrates sparse and dense dimensionality reduction techniques. We derive the convergence rate of our estimator under misspecification due to the MIDAS approximation error, τ-mixing dependence, and polynomial tails. Our method’s finite sample performanc…
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…
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…
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…
Factor-Augmented Sparse Midas Regressions with an Application to Nowcasting
This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our novel approach integrates sparse and dense dimensionality reduction techniques. We derive the convergence rate of our estimator under misspecification due to the MIDAS approximation error, τ-mixing dependence, and polynomial tails. Our method’s finite sample performanc…
Monetary Policy and Economic Impact (3 works) · Nowcasting (3 works) · Complex Systems and Time Series Analysis (2 works) · Computer Science (2 works) · Econometrics (2 works) · Estimator (2 works) · Machine learning (2 works) · Mathematics (2 works) · Regression (2 works) · Statistics (2 works)