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

See also

Functions adf(), KPSS_1break(), KPSS_2breaks(), dfgls(), erspt()