adf_2breaks#

Purpose#

Computes the Augmented Dickey-Fuller unit root test with two structural breaks.

Format#

{ tstat, tb1, tb2, lags, cv } = adf_2breaks(y, model[, pmax, ic, trimm])#

Parameters:

y (Nx1 matrix) – Time series data to be tested.
model (Scalar) –
Model to be implemented.

1

Break in level.

2

Break in level and trend.
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.

Returns:

tstat (Scalar) – Minimum test statistic.
tb1 (Scalar) – Location of first break.
tb2 (Scalar) – Location of second break.
lags (Scalar) – Number of lags selected by chosen information criterion.
cv (Vector) – 1%, 5%, and 10% critical values for adf_2breaks() t-stat.

Examples#

library tspdlib;

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

// Break in level
model = 1;

// Run lags
{ ADF_min, tb1, tb2, lags, cv } = adf_2breaks(y, model);

Source#

adf_2br.src

1	Break in level.
2	Break in level and trend.

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