# It seems that Arabica Granger-causes Robusta prices, but not the other way around. #Wald.test (H0: Arabica does not Granger-cause Robusta) #Wald-test (H0: Robusta does not Granger-cause Arabica) #VAR-Model, lag=7 (additional lag, though not tested) # The test can be directly done with the VAR model, however using the correct # Model with p=6 is less likely to be serially correlated. # Since first order differencing eliminates the unit root, the maximum order of integration Plot(as.Date(cof1$Date),cof1$Arabica,type="l",col="black",lwd=2,ylim=range(cof1$Robusta)) Therefore only values from 1976:01 onwards are regarded Lines(as.Date(cof$Date),cof$Robusta,col="blue",lty=2,lwd=1) Plot(as.Date(cof$Date),cof$Arabica,type="l",col="black",lwd=2) List.packages <- c("fUnitRoots", "urca", "vars", "aod", "zoo", "tseries") The script is annotated, but let me know if I can clarify anything or if there is room for improvement. The script below tests for causality between these two time series. The example is about causalities between prices in Robusta and Arabica coffee. However, Toda and Yamamoto (1995) noted that one advantage of the TY-method is that you don’t have to test for cointegration and, therefore, a pretest bias can be avoided.
#Granger causality test in stata series
If series are cointegrated, there must be a causality. You may want to do a test of cointegration. Carry out a Wald test for the first p variables only with p degrees of freedom. This is the augmented VAR-model, VAR(p+m).Ħ. Add the maximum order of integration to the number of lags.
#Granger causality test in stata serial
Carry out tests for misspecification, especially for residual serial correlation.ĥ.
Set up a VAR-model in the levels (do not difference the data).ģ. If none of the series in integrated, the usual Granger-causality test can be done.Ģ. Test for integration (structural breaks need to be taken into account).
The procedure is based on the following steps:ġ. In this post, I will show how Professor Giles’ example can be implemented in R. More formal explanations can be found in the original TY (1995) paper. Professor Giles gives an excellent example of how the TY method can be implemented.
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