mgls¶
Purpose¶
Computes the MGLS unit root test.
Format¶
-
{ MZa, MZt, MSB, MPT, cvMZA, cvMZt, cvMSB, cvMPT } =
MGLS(y, model[, bwl, varm])¶ Parameters: - y (Nx1 matrix) – Time series data to be tested.
- model (Scalar) –
Model to be implemented.
1 Constant. 2 Constant and trend. - bwl (Scalar) – Optional, bandwidth for the spectral window. Default = round(4 * (T/100)^(2/9)).
- varm (Scalar) –
Optional, long-run consistent variance estimation method. Default = 1.
1 iid. 2 Bartlett. 3 Quadratic Spectral (QS). 4 SPC with Bartlett (Sul, Phillips & Choi, 2005) 5 SPC with QS 6 Kurozumi with Bartlett 7 Kurozumi with QS
Returns: - MZa (Scalar) – MZalpha test statistic.
- MZt (Scalar) – MZt test statistic.
- MSB (Scalar) – MSB test statistic.
- MPT – MPT test statistic.
- cvMZa (Scalar) – 1%, 5%, and 10% critical values for MZa.
- cvMZt (Vector) – 1%, 5%, and 10% critical values for MZt.
- cvMSB (Vector) – 1%, 5%, and 10% critical values for MSB.
- cvMPT (Vector) – 1%, 5%, and 10% critical values for MPT.
Examples¶
library tspdlib;
// Load date file
y = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/ts_examples.csv",
"Y + date($Date, '%b-%y')");
// With constant
model = 1;
{MZa, MZt, MSB, MPT, cvMZA, cvMZt, cvMSB, cvMPT} = MGLS(y, model);
// With constant and trend
model = 2;
{MZa, MZt, MSB, MPT, cvMZA, cvMZt, cvMSB, cvMPT} = MGLS(y, model);