8 citations · 19 across the 15 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
Local Projection Inference in High Dimensions
Robert Adamek, Stephan Smeekes, Ines Wilms
In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local pro…
Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
Alain Hecq, Marie Ternes, Ines Wilms
Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies. However, as the number of series and high-frequency observat…
bootUR: An R Package for Bootstrap Unit Root Tests
Stephan Smeekes, Ines Wilms
Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing in th…