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