ralslm_breaks¶
Purpose¶
Computes the Augmented Dickey-Fuller unit root test with 1 or 2 breaks and the RALS technique for non-normal errors.
Format¶
-
{ LM_min, tb, p, cv_lm, RALS_lm, p2, cv_ralslm } =
ralslm_breaks(y, model, nbreaks[, pmax, ic, trimm])¶ Parameters: - y (Nx1 matrix) – Time series data to be tested.
- model (Scalar) –
Model to be implemented.
1 Level break (Meng, Im, Lee, & Tieslau (2014)) 2 Level and trend break (Meng, Lee, & Payne (2017)) - pmax (Scalar) – Optional, the maximum number of lags for \(\Delta y\). Default = 8.
- ic (Scalar) –
Optional, the information criterion used for choosing lags. Default = 3.
1 Akaike. 2 Schwarz. 3 t-stat significance. - trimm (Scalar) – Optional, trimming rate. Default = 0.10.
Nbreaks: Number of breaks (1 or 2).
Returns: - LM_min (Scalar) – Minimum LM statistic without RALS terms.
- tb (Vector) – Location of estimated breaks.
- p (Scalar) – Optimal number of lags selected by determined information criterion.
- cv_lm (Vector) – 1%, 5%, 10% critical values for LM test based on response surfaces.
- RALS_lm (Scalar) – LM statistic based on RALS procedure and breaks.
- rho2 (Scalar) – The estimated rho square.
- cv_ralslm (Vector) – 1%, 5%, 10% critical values for RALS-LM test basen the estimated rho2.
Examples¶
library tspdlib;
// Load date file
y = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/TSe.dat");
// With constant
model = 1;
{ LM_min, tb, p, cv_lm, RALS_lm, rho2, cv_ralslm } = RALSLM_breaks(y, model, nbreaks);