Decoding Causality by Fictitious VAR Modeling

TL;DR

This paper introduces a novel fictitious VAR modeling approach to identify causality in multivariate time series, distinguishing cause-effect relations from mere correlations with high accuracy, and applies it to climate change data.

Contribution

The paper proposes a new equilibrium-based causality measure called causality distribution, enabling accurate detection of cause-effect relations in multivariate time series.

Findings

High accuracy in simulation studies for causality detection

Effective application to climate change causal analysis

Zero causality hypothesis test for endogenous variable identification

Abstract

In modeling multivariate time series for either forecast or policy analysis, it would be beneficial to have figured out the cause-effect relations within the data. Regression analysis, however, is generally for correlation relation, and very few researches have focused on variance analysis for causality discovery. We first set up an equilibrium for the cause-effect relations using a fictitious vector autoregressive model. In the equilibrium, long-run relations are identified from noise, and spurious ones are negligibly close to zero. The solution, called causality distribution, measures the relative strength causing the movement of all series or specific affected ones. If a group of exogenous data affects the others but not vice versa, then, in theory, the causality distribution for other variables is necessarily zero. The hypothesis test of zero causality is the rule to decide a variable is endogenous or not. Our new approach has high accuracy in identifying the true cause-effect relations among the data in the simulation studies. We also apply the approach to estimating the causal factors' contribution to climate change.