pd_kpss¶

Purpose¶

Computes the panel data KPSS test.

Format¶

{ testd_hom, testd_het, m_lee_est, brks } = pd_kpss(y, model[, nbreak, bwl, varm, pmax, bCtl])¶

Parameters:

y (TxN matrix) – Wide format panel data.
model (Scalar) –
Model to be implemented.

1

Constant (Hadri test).

2

Constant and trend (Hadri test).

3

Shift in the mean.

4

Shift in mean and trend.
nbreak (Scalar) – Optional, number of breaks to consider (up to 5). Default = 5.
bwl (Scalar) – Optional, bandwidth for the spectral window. Default = round(4 * (T/100)^(2/9)).
varm (Scalar) –
Optional, long-run consistent variance estimation method. Default = 1.

1

iid.

2

Bartlett.

3

Quadratic Spectral (QS).

4

SPC with Bartlett (Sul, Phillips & Choi, 2005)

5

SPC with QS

6

Kurozumi with Bartlett

7

Kurozumi with QS

Returns:

test_hom (Scalar) – Panel test statistic assuming homogenous variance.
test_het (Scalar) – Panel test statistic assuming heterogenous variance.
m_lee_est (Matrix) – Contains results for each individual group. First column contains KPSS test statistic, second column contains the optimal number of lags selected using the mlwz criteria.
brks (Scalar) – Estimated breaks. Breaks for each individual group are contained in separate rows.

Examples¶

new;
cls;
library tspdlib;

// Load data
data = loadd(__FILE_DIR $+ "pd_gdef.gdat");
data = setColDateFormats(data, "%Y", "Year");

// Set model to  hve break in constant and trend
model = 4;

// Compute panel data test
{ test_hom, test_het, kpsstest, m_br} = pd_kpss(data, model);

// Plot results
plotPDKPSS(data, m_br, kpsstest);

Source¶

pd_kpss.src

1	Constant (Hadri test).
2	Constant and trend (Hadri test).
3	Shift in the mean.
4	Shift in mean and trend.

1	iid.
2	Bartlett.
3	Quadratic Spectral (QS).
4	SPC with Bartlett (Sul, Phillips & Choi, 2005)
5	SPC with QS
6	Kurozumi with Bartlett
7	Kurozumi with QS