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Found 9 papers, 1 papers with code
High-Dimensional Causality for Climatic Attribution
In this paper we test for Granger causality in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcings and global temperatures.
Inference in Non-stationary High-Dimensional VARs
We combine this lag augmentation with a post-double-selection procedure in which a set of initial penalized regressions is performed to select the relevant variables for both the Granger causing and caused variables.
Sparse High-Dimensional Vector Autoregressive Bootstrap
We introduce a high-dimensional multiplier bootstrap for time series data based capturing dependence through a sparsely estimated vector autoregressive model.
Local Projection Inference in High Dimensions
In this paper, we estimate impulse responses by local projections in high-dimensional settings.
Min(d)ing the President: A text analytic approach to measuring tax news
We propose a novel text-analytic approach for incorporating textual information into structural economic models and apply this to study the effects of tax news.
bootUR: An R Package for Bootstrap Unit Root Tests
Unit root tests form an essential part of any time series analysis.
Lasso Inference for High-Dimensional Time Series
In this paper we develop valid inference for high-dimensional time series.
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 space models using a dynamic factor approach to incorporate auxiliary information from high-dimensional data sources.
A Residual Bootstrap for Conditional Value-at-Risk
A fixed-design residual bootstrap method is proposed for the two-step estimator of Francq and Zako\"ian (2015) associated with the conditional Value-at-Risk.
