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);
Source#
gls.src