Decoding Causality by Fictitious VAR Modeling
This addresses the challenge of distinguishing causality from correlation in time series analysis for fields like forecasting and policy, though it appears incremental as it builds on existing VAR modeling approaches.
The paper tackles the problem of discovering cause-effect relations in multivariate time series, proposing a fictitious vector autoregressive model to identify long-run relations and filter out spurious ones, with results showing high accuracy in simulations and application to climate change factor estimation.
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.