adf#

Purpose#

Computes the Augmented Dickey-Fuller unit root test.

Format#

{ tstat, lags, cv } = adf(y, model[, pmax, ic])#

Parameters:

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

0

No constant or trend.

1

Constant.

2

Constant 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.

Returns:

tstat (Scalar) – Dickey-Fuller test statistic.
lags (Scalar) – Number of lags selected by chosen information criterion.
cv (Vector) – 1%, 5%, and 10% critical values for ADF \(\tau\)-stat.

Examples#

library tspdlib;

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

// No deterministic component
model = 0;

// Call ADF test
{ ADFtau, ADFp, cvADF } = ADF(y, model);

Source#

adf.src

0	No constant or trend.
1	Constant.
2	Constant and trend.

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