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

See also

Functions kpss_1break(), kpss_2breaks(), lmkpss()

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