coint_pouliaris#

Purpose#

Computes residual based tests for cointegration with asymptotic critical values.

Format#

{ Zt, Za, cvZt, cvZa } = coint_pouliaris(y, x, model[, bwl, varm])#
Parameters:

  • y (Nx1 matrix) – Dependent variable.

  • x (NxK matrix) – Independent variable.

  • model (Scalar) –

    Model to be implemented.

    0

    None

    1

    Constant only

    2

    Constant and trend

  • bwl (Scalar) – Optional, bandwidth length for long-run variance computation. 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:

  • Zt (Scalar) – Phillips & Ouliaris (1989) Zt test

  • Za (Scalar) – Phillips & Ouliaris (1989) Za test

  • cvZt (Scalar) – 1%, 5%, 10% critical values for Zt test statistic.

  • cvZa (Scalar) – 1%, 5%, 10% critical values for Za test statistic.

Examples#

new;
cls;
library tspdlib;

// Load dataset
data = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/ts_coint.csv",
                          "Y1 + Y2 + Y3 + Y4 + date($Date, '%b-%y')");


// Define y and x matrix
y = data[., 1];
x = data[., 2:cols(data)];

// No constant or trend
model = 0;

// Call test
{ Zt, Za, cvZt, cvZa } = coint_pouliaris(y, x, model);

Source#

coint_pouliaris.src

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