I have three macro economic variables (ICS - consumer sentiment, ER - employment rate, DGO - durable goods order) and have run Granger causality tests in R on them. I don't really know how to interpret the results of a Granger test. Could anyone give me a hand with making some sense of the results?
I know that we are checking to see if one variable can be used to predict another and I understand that if that is true then there must be some lag in one of the variables and that the order of the Granger test has to do with the order. I don't know how to interpret the fact that 2 models are reported here. I can see that one model is with the regressor variable and the other model is without the regressor. I assume the Lags vector 1:3 means that we are testing 1, 2,and 3 month lags.
grangertest(ICS~ER, order = 3, data=modeling.mts)
Granger causality test
Model 1: ICS ~ Lags(ICS, 1:3) + Lags(ER, 1:3)
Model 2: ICS ~ Lags(ICS, 1:3)
Res.Df Df F Pr(>F)
1 258
2 261 -3 2.0352 0.1094
grangertest(ICS~DGO, order = 3, data=modeling.mts)
Granger causality test
Model 1: ICS ~ Lags(ICS, 1:3) + Lags(DGO, 1:3)
Model 2: ICS ~ Lags(ICS, 1:3)
Res.Df Df F Pr(>F)
1 258
2 261 -3 4.8621 0.002625 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
grangertest(DGO~ER, order = 3, data=modeling.mts)
Granger causality test
Model 1: DGO ~ Lags(DGO, 1:3) + Lags(ER, 1:3)
Model 2: DGO ~ Lags(DGO, 1:3)
Res.Df Df F Pr(>F)
1 258
2 261 -3 3.2704 0.02181 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1