coint_cissano

Purpose

Lagrange Multiplier‐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_cissano(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(__FILE_DIR $+ "ts_coint.csv",
                          "Y1 + Y2 + Y3 + Y4 + date($Date, '%b-%y')");


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

// Level shifts
model = 1;
{ SCols, TBols, SCdols, TBdols, lambda, cv } = coint_cissanso(y, x, model);

Source

coint_cissano.src

See also

Functions coint_egranger()