qr_adf#

Purpose#

Computes the quantile Augmented Dickey-Fuller unit root test.

Format#

{ qr_tstat, lags, cv } = qr_ADF(y, model, tau[, pmax, ic])#

Parameters:

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

1

Constant.

2

Constant and trend.
tau (Scalar) – The quantile (\(0 \lt \tau \lt 1\)).
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:

qr_tstat (Scalar) – Quantile, Dickey-Fuller test statistic.
p (Scalar) – Chosen number of lags.
cv (Vector) – 1%, 5%, 10% critical values given the estimated delta2

Examples#

library tspdlib;

// Load date file
y = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/TSe.dat");

// Run model with constant
model = 1;

// Test for 70% percentile
tau = 0.7;

// Run test
{ stat1, p1, cv1 } = qr_ADF(y, model, tau);

Source#

qr_adf.src

1	Constant.
2	Constant and trend.

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