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);

Source¶

kpss.src

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

1	Constant.
2	Constant and trend.