coint_cissanso

Purpose

Computes a LM-type statistic to test the null hypothesis of cointegration allowing for the possibility of a structural break.

Format

{ SCols, TBols, SCdols, TBdols, lambda, cv } = coint_cissanso(y, x, model[, bwl, varm, trimm, q])

Parameters:

• y (Nx1 matrix) – Dependent variable.

• x (NxK matrix) – Independent variable.

• model (Scalar) –

  Model to be implemented.

  1	Model An: Level shift.
  2	Model A: Level shift with trend.
  3	Model D: Regime shift.
  4	Model E: Regime and trend shift.

• bwl (Scalar) – Optional, bandwidth length for long-run variance computation. 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

• trimm (Scalar) – Optional, trimming rate. Default = 0.10.

• q (Scalar) – Optional, number of leads and lags for DOLS estimation. Default = int(4 * (t/100)^(2/9)).

Returns:

• SCols (Scalar) – SC test based on OLS estimation

• TBols (Scalar) – Break location based on OLS estimation.

• SDols (Scalar) – SC test based on DOLS estimation

• TBDols (Scalar) – Break location based on DOLS estimation.

• lamdba – Fraction of break (TB/T)

• cv (Vector) – 1%, 5%, 10% critical values for the chosen model

Examples

library tspdlib;

// Load dataset
data = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/ts_coint.csv",
                                 ". + date($Date, '%b-%y')");

// Define y and x matrix
y = data[., 1];
x = data[., 2:cols(data)];

// Level shifts
model = 1;

// Call test
{ SCols, TBols, SCdols, TBdols, lambda, cv } = coint_cissanso(y, x, model);

Source

coint_cissano.src

See also

Functions coint_egranger(), coint_ghansen(), coint_hatemij(), coint_maki()