### Abstract

Original language | English |
---|---|

Publisher | arXiv.org at Cornell University Library |

Publication status | Published - 28 Feb 2019 |

### Keywords

- Granger causality
- post-double-selection
- vector autoregressive models
- high-dimensional inference

### Cite this

*Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure*. arXiv.org at Cornell University Library.

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**Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure.** / Hecq, Alain; Margaritella, Luca; Smeekes, Stephan.

Research output: Working paper › Professional

TY - UNPB

T1 - Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure

AU - Hecq, Alain

AU - Margaritella, Luca

AU - Smeekes, Stephan

PY - 2019/2/28

Y1 - 2019/2/28

N2 - In this paper we develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test which retains the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out the effects of the variables not of interest. We conduct an extensive set of Monte-Carlo simulations to compare different ways to set up the test procedure and choose the tuning parameter. The test performs well under different data generating processes, even when the underlying model is not very sparse. Additionally, we investigate two empirical applications: the money-income causality relation using a large macroeconomic dataset and networks of realized volatilities of a set of 49 stocks. In both applications we find evidences that the causal relationship becomes much clearer if a high-dimensional VAR is considered compared to a standard low-dimensional one.

AB - In this paper we develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test which retains the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out the effects of the variables not of interest. We conduct an extensive set of Monte-Carlo simulations to compare different ways to set up the test procedure and choose the tuning parameter. The test performs well under different data generating processes, even when the underlying model is not very sparse. Additionally, we investigate two empirical applications: the money-income causality relation using a large macroeconomic dataset and networks of realized volatilities of a set of 49 stocks. In both applications we find evidences that the causal relationship becomes much clearer if a high-dimensional VAR is considered compared to a standard low-dimensional one.

KW - Granger causality

KW - post-double-selection

KW - vector autoregressive models

KW - high-dimensional inference

M3 - Working paper

BT - Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure

PB - arXiv.org at Cornell University Library

ER -