Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure
We develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out effects of nuisance variables and establish its uniform asymptotic validity. We conduct an extensive set of Monte-Carlo simulations that show our tests perform well under different data generating processes, even without sparsity. We apply our testing procedure to find networks of volatility spillovers and we find evidence that causal relationships become clearer in high-dimensional compared to standard low-dimensional VARs.
Code (1)
Tasks
Variable SelectionSimilar Papers 제목 키워드 기반
grangersearch: An R Package for Exhaustive Granger Causality Testing with Tidyverse Integration
This paper introduces grangersearch, an R package for performing exhaustive Granger causality searches on multiple time series. The package provides: (1) exhaustive pairwise search across multiple variables, (2) automati…
Inference in Non-stationary High-Dimensional VARs
In this paper we construct an inferential procedure for Granger causality in high-dimensional non-stationary vector autoregressive (VAR) models. Our method does not require knowledge of the order of integration of the ti…
Time SeriesTime Series AnalysisVocal Bursts Intensity PredictionUncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference
Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “G…
Causal InferenceTime SeriesTime Series AnalysisHigh-Dimensional Granger 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. By allowing f…
Time SeriesTime Series AnalysisVocal Bursts Intensity PredictionHigh-Dimensional Granger Causality Tests with an Application to VIX and News
We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asympto…
regressionTime SeriesTime Series AnalysisTime Series Regression+1