pd_cause

Purpose

Computes tests for Granger causality in heterogeneous mixed panels with bootstrap critical values.

Format

pd_stat = pd_cause(data, Ncross, test[, pmax, dmax, ic, Nboot, vnames])

Parameters:

• data (Txk matrix) – Data to be tested with k individual variables each in a separate column.

• Ncross (Scalar) – Number of cross sections.

• test (String) –

  The panel data causality test to be implemented.

  ”fisher”	Fisher test.
  ”zhnc”	Panel Zhnc statistic.
  ”surwald”	Panel SUR Wald statistic.

• pmax (Scalar) – Optional, maximum number of lags. Default = 8.

• dmax (Scalar) – Optional, maximum integration degree of variables. Default = 1.

• ic (Scalar) –

  Optional, the information criterion used for choosing lags. Default = 1.

  1	Akaike.
  2	Schwarz.
  3	t-stat significance.

  Default = 2.

• Nboot (Scalar) – Optional, Number of bootstrap replications. Default = 1000.

• vnames (String array) – Variable names.

Returns:

cause_stat (Dataframe) – Panel causation statistics. Prints individual results and bootstrap critical values.

Examples

library tspdlib;

// Load data
data = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/pdcause.dat");

// Number of cross-sections
N = 9;

/*
** Run Fisher test
*/
test = "fisher";

// Call test
cause_stat = pd_cause(data, N, test);

/*
** Run Zh and Zn test
*/
test = "zhnc";

// Call test
cause_stat = pd_cause(data, N, test);

/*
** Run SURwald test
*/
test = "surwald";

// Call test
cause_stat = pd_cause(data, N, test);

Source

pd_cause.src

See also

Functions granger(), panel_fisher(), panel_zhnc(), panel_surwald()