4 citations · 4 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
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…
Lasso Inference for High-Dimensional Time Series
Robert Adamek, Stephan Smeekes, Ines Wilms
In this paper we develop valid inference for high-dimensional time series. We extend the desparsified lasso to a time series setting under Near-Epoch Dependence (NED) assumptions a…