Publications
Performance of radio-based detection for operational monitoring of M5+ class solar flares
Journal of Space Weather and Space Climate (2026)
Early detection of major solar flares is critical due to their potential to disturb radar and radio systems. Typically, soft X-ray flux is used to monitor and classify solar flares, but since this flux has to be measured in space, it means that its availability itself is dependent on space weather conditions. For this reason, in this paper, we investigated the feasibility of using ground radio observations to monitor major (M5+ class) solar flares. We made use of datasets from the GOES-16 satellite and the Radio Solar Telescope Network in the time range between March 2023 and March 2025. An elastic net regularized logistic regression model was trained on this data, optimized through a grid search and with incorporated class weighting for class imbalance. It was found that especially higher frequencies (8800 MHz) had a reasonable ability for monitoring and predicting major flares (precision and recall for flare events are 53% and 65%, respectively– implying that roughly one third of flares were not detected –with signals appearing, on average, 3 to 4 minutes before the M5 threshold is exceeded). Radio measurements at super high frequencies can thus serve as an alternative method to monitor major solar flaring activity.
Decomposing Co-Movements in Matrix-Valued Time Series: A Pseudo-Structural Reduced Rank Approach
Econometrics and Statistics (2026)· Slides
A pseudo-structural framework is proposed for analyzing contemporaneous co-movements in stationary reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector-autoregressive (VAR) models that would discard the matrix structure, the formulation preserves it, enabling a decomposition of co-movements into three interpretable components: row-specific, column-specific, and joint (row–column) interactions across the matrix-valued time series. The estimator admits standard asymptotic inference and a BIC-type criterion is proposed for the joint selection of the reduced ranks and the autoregressive lag order. The method’s finite-sample performance in terms of estimation accuracy, coverage and rank selection is validated through simulation experiments, including cases of rank misspecification. Practical usefulness is illustrated through the application of the pseudo-structural approach to distill coincident indicators from raw labor market data across nine Midwestern U.S. states, uncovering distinct row-, column-, and joint co-movement patterns that reflect both within-state labor market dynamics and cross-state linkages.
Detecting Cointegrating Relations in Matrix-Valued Time Series
Economics Letters (2025) · Slides
This paper proposes a Matrix Error Correction Model to identify cointegration relations in matrix-valued time series. We hereby allow separate cointegrating relations along the rows and columns of the matrix-valued time series and use information criteria to select the cointegration ranks. Through Monte Carlo simulations and a macroeconomic application, we demonstrate that our approach provides a reliable estimation of the number of cointegrating relationships.
Working Papers
Impulse Response Inference in Matrix Autoregressions
arXiv:2610.05405 · Slides
Matrix autoregressive (MAR) models offer a parsimonious framework for modeling matrix-valued time series, yet tools for estimation and inference for their impulse response functions are lacking. We develop asymptotic and bootstrap-based inference for impulse responses of stable MAR(\(p\)) models. We derive the joint asymptotic distribution of the coefficient and covariance estimators, which permits closed-form delta-method standard errors. To address finite-sample bias, we propose ProBAB-MAR, a bias-corrected bootstrap that projects the corrected coefficients back onto the Kronecker parameter space, and prove that it attains asymptotically correct coverage. Monte Carlo simulations show that delta-method intervals undercover in small samples, while ProBAB-MAR achieves near-nominal coverage with intervals considerably narrower than those from an unrestricted VAR. An application to euro area inflation illustrates how shocks transmit across countries and inflation categories.