research
∙
02/02/2023
Sparse High-Dimensional Vector Autoregressive Bootstrap
We introduce a high-dimensional multiplier bootstrap for time series dat...
share
research
∙
09/07/2022
Local Projection Inference in High Dimensions
In this paper, we estimate impulse responses by local projections in hig...
research
∙
07/23/2020
bootUR: An R Package for Bootstrap Unit Root Tests
Unit root tests form an essential part of any time series analysis. We p...
research
∙
07/21/2020
Lasso Inference for High-Dimensional Time Series
The desparsified lasso is a high-dimensional estimation method which pro...
research
∙
03/13/2019
Nonparametric estimation and bootstrap inference on trends in atmospheric time series: an application to ethane
Understanding the development of trends and identifying trend reversals ...
research
∙
02/28/2019
Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure
In this paper we develop an LM test for Granger causality in high-dimens...
research
∙
01/31/2019
A dynamic factor model approach to incorporate Big Data in state space models for official statistics
In this paper we consider estimation of unobserved components in state s...
research
∙
09/24/2018
An Automated Approach Towards Sparse Single-Equation Cointegration Modelling
In this paper we propose the Single-equation Penalized Error Correction ...
research
∙
07/06/2018
