8 citations · 18 across the 12 of their papers we have counts for
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econ.EM2023★ 1 cited
Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
Yu Jeffrey Hu, Jeroen Rombouts, Ines Wilms
On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper devel…
econ.EM2023
Sparse High-Dimensional Vector Autoregressive Bootstrap
Robert Adamek, Stephan Smeekes, Ines Wilms
We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consi…
econ.EM2023
Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions
Alain Hecq, Marie Ternes, Ines Wilms
Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables. However, due to the periodic structu…