2 citations · 2 across the 5 of their papers we have counts for
6 papers
Impulse Response Inference for Matrix Autoregressions
Alain Hecq, Ivan Ricardo, Ines Wilms
Matrix autoregressive (MAR) models offer a parsimonious framework for modeling matrix-valued time series, yet tools for estimation and inference for their impulse response function…
Performance of radio-based detection to operational monitoring M5+ class solar flares
Julia van Ravenswaaij, Ines Wilms, Ivan Ricardo +1
Early detection of major solar flares is critical for defense operations due to their potential to disturb radar and radio systems. Typically, soft X-ray flux is used to monitor an…
Sparse Tree-Based Aggregation for Time Series Regressions
Marie Corillon, Stephan Smeekes, Ines Wilms
High-dimensional time series regressions are often regularized to produce sparse coefficients. We show that temporal aggregation provides a powerful alternative to reduce dimension…
Decomposing Co-Movements in Matrix-Valued Time Series: A Pseudo-Structural Reduced-Rank Approach
Alain Hecq, Ivan Ricardo, Ines Wilms
A pseudo-structural framework is proposed for analyzing contemporaneous co-movements in stationary reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector aut…
Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series
Alain Hecq, Ivan Ricardo, Ines Wilms
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 row…
Reduced-Rank Matrix Autoregressive Models: A Medium Approach
Alain Hecq, Ivan Ricardo, Ines Wilms
Reduced-rank regressions are powerful tools used to identify co-movements within economic time series. However, this task becomes challenging when we observe matrix-valued time ser…