lmkpss¶
Purpose¶
Computes the Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) stationarity test.
Format¶
-
{ kpss, cv } =
LMkpss(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: - kpss (Scalar) – The KPSS test statistic.
- cvKPSS (Scalar) – 1%, 5%, and 10% critical values for the KPSS test statistic.
Examples¶
library tspdlib;
// Load date file
y = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/ts_examples.csv", "Y");
// Constant
model = 1;
{ kpss, cvKPSS } = lmkpss(y, model);
// Constant and trend
model = 2;
{ kpss, cvKPSS } = lmkpss(y, model);