::Free Statistics and Forecasting Software::

All rights reserved. The non-commercial (academic) use of this software is free of charge. The only thing that is asked in return is to cite this software when results are used in publications.

This free online software (calculator) computes the bivariate Granger causality test in two directions. Enter the time series in the respective data boxes and specify the Box-Cox tranformation parameter, the degree of non-seasonal differencing, and the degree of seasonal differencing (for each time series) to induce stationarity. The maximum number lags (of the endogenous variable) that is used in the test equation can also be specified.

Enter (or paste) your data delimited by hard returns.

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Source code of R module

library(lmtest) par1 <- as.numeric(par1) par2 <- as.numeric(par2) par3 <- as.numeric(par3) par4 <- as.numeric(par4) par5 <- as.numeric(par5) par6 <- as.numeric(par6) par7 <- as.numeric(par7) par8 <- as.numeric(par8) ox <- x oy <- y if (par1 == 0) { x <- log(x) } else { x <- (x ^ par1 - 1) / par1 } if (par5 == 0) { y <- log(y) } else { y <- (y ^ par5 - 1) / par5 } if (par2 > 0) x <- diff(x,lag=1,difference=par2) if (par6 > 0) y <- diff(y,lag=1,difference=par6) if (par3 > 0) x <- diff(x,lag=par4,difference=par3) if (par7 > 0) y <- diff(y,lag=par4,difference=par7) print(x) print(y) (gyx <- grangertest(y ~ x, order=par8)) (gxy <- grangertest(x ~ y, order=par8)) bitmap(file="test1.png") op <- par(mfrow=c(2,1)) (r <- ccf(ox,oy,main="Cross Correlation Function (raw data)",ylab="CCF",xlab="Lag (k)")) (r <- ccf(x,y,main="Cross Correlation Function (transformed and differenced)",ylab="CCF",xlab="Lag (k)")) par(op) dev.off() bitmap(file="test2.png") op <- par(mfrow=c(2,1)) acf(ox,lag.max=round(length(x)/2),main="ACF of x (raw)") acf(x,lag.max=round(length(x)/2),main="ACF of x (transformed and differenced)") par(op) dev.off() bitmap(file="test3.png") op <- par(mfrow=c(2,1)) acf(oy,lag.max=round(length(y)/2),main="ACF of y (raw)") acf(y,lag.max=round(length(y)/2),main="ACF of y (transformed and differenced)") par(op) dev.off() load(file="createtable") a<-table.start() a<-table.row.start(a) a<-table.element(a,"Granger Causality Test: Y = f(X)",5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"Model",header=TRUE) a<-table.element(a,"Res.DF",header=TRUE) a<-table.element(a,"Diff. DF",header=TRUE) a<-table.element(a,"F",header=TRUE) a<-table.element(a,"p-value",header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"Complete model",header=TRUE) a<-table.element(a,gyx$Res.Df[1]) a<-table.element(a,"") a<-table.element(a,"") a<-table.element(a,"") a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"Reduced model",header=TRUE) a<-table.element(a,gyx$Res.Df[2]) a<-table.element(a,gyx$Df[2]) a<-table.element(a,gyx$F[2]) a<-table.element(a,gyx$Pr[2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file="mytable1.tab") a<-table.start() a<-table.row.start(a) a<-table.element(a,"Granger Causality Test: X = f(Y)",5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"Model",header=TRUE) a<-table.element(a,"Res.DF",header=TRUE) a<-table.element(a,"Diff. DF",header=TRUE) a<-table.element(a,"F",header=TRUE) a<-table.element(a,"p-value",header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"Complete model",header=TRUE) a<-table.element(a,gxy$Res.Df[1]) a<-table.element(a,"") a<-table.element(a,"") a<-table.element(a,"") a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"Reduced model",header=TRUE) a<-table.element(a,gxy$Res.Df[2]) a<-table.element(a,gxy$Df[2]) a<-table.element(a,gxy$F[2]) a<-table.element(a,gxy$Pr[2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file="mytable2.tab")

Cite this software as:

Wessa P., (2016), Bivariate Granger Causality (v1.0.4) in Free Statistics Software (v1.2.1), Office for Research Development and Education, URL http://www.wessa.net/rwasp_grangercausality.wasp/

The R code is based on :

Anil K Seth (2007) Granger causality.Scholarpedia, 2(7):1667

To cite Wessa.net in publications use:
Wessa, P. (2024), Free Statistics Software, Office for Research Development and Education,
version 1.2.1, URL https://www.wessa.net/

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