Interpreting VAR Granger Causality on Eviews

Question

I am currently conducting a multivariate time series analysis on Eviews. I am investigating the causal relation among various economic variables. I have estimated a VAR model using the Toda-Yamamoto Procedure, following the protocol described by Dr Giles in his blog, Econometrics Beat. I need help with some of the interpretation of my results. I know how to interpret the p-value results in terms of the null hypothesis. However, I wanted to know what is the direction of the causality (unidirectional or bidirectional). Or to get directional information of the relations among variables I have to look somewhere else other than he Granger Causality results? Here are the results: [![Granger/Wald Test][3]][3]

The variables are log transformed and they are:

1. ln_gdp54: gross domestic product
2. ln_gdfi54: gross domestic fixed investment
3. ln_lf: labor force
4. ln_mmtoe: energy consumption (million oil tons equivalent)
5. ln_totco2: total CO2 emissions

Thank for the help!

Answer 1

I cannot guarantee whether the test has been carried out correctly, but here is what I can read in the table.

First block: the null hypothesis ln_totco2 $\not \xrightarrow{G}$ ln_mmtoe should not be rejected at the regular 5% level as the associated p-value is as high as 0.3301 (way above 0.05).

Second block: the null hypothesis ln_mmtoe $\not \xrightarrow{G}$ ln_totco2 should not be rejected at the regular 5% level as the associated p-value is as high as 0.1776 (way above 0.05).

x $\not \xrightarrow{G}$ y means "x does not Granger-cause y".

In sum, there is not enough evidence for causality either way. To answer the question directly, there is not enough evidence either for unidirectional or for bidirectional causality.