granger¶
Purpose¶
Computes the tests for Granger causality of specified variables.
Format¶
-
GC_out =
granger(data, test[, pmax, ic, Nboot, vnames])¶ - Parameters:
data (Txk matrix) – Data to be tested with k individual variables each in a separate column.
test (Scalar) –
Test option for Granger causality
0
Granger causality.
1
Toda & Yamamoto
2
Single Fourier-frequency Granger causality.
4
Single Fourier frequency Toda & Yamamoto.
5
Cumulative Fourier-frequency Toda & Yomamoto
pmax (Scalar) – Optional, maximum number of lags. Default = 8.
ic (Scalar) –
Optional, the information criterion used for choosing lags.
1
Akaike.
2
Schwarz.
3
t-stat significance.
Default = 2.
Nboot (Scalar) – Number of bootstrap replications.
vnames (String array) – Variable names. Default = dataframe variable names OR “X1”$|”X2”.
- Returns:
GC_out (Kx5 Matrix) – Results matrix containing Wald stat~P-values~Bootstrap P-values~Lags~Frequency
Examples¶
library tspdlib;
// Load data matrix
GCdata = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/TScaus.dat");
data = ln(GCdata);
// Toda & Yamamoto test
test = 1;
// Run test
GC_out = granger(data, test);
Source¶
gctests.src