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);