Testing for Granger non-causality using the autoregressive metric

- Granger causality tests among time series have become ubiquitous in econometric.
- The use of non-stationary data in causality tests can yield spurious causality.
- It is important to establish the stochastic properties of the time series involved.
- We propose a new Granger causality test.
- It can be carried out irrespective of whether the variables are stationary or not.

A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in finite samples. The empirical relevance of the test is illustrated via an application.