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