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);