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);

Source#

rals_lm_breaks.src

1	Level break (Meng, Im, Lee, & Tieslau (2014))
2	Level and trend break (Meng, Lee, & Payne (2017))

1	Akaike.
2	Schwarz.
3	t-stat significance.