lmkpss
==============================================

Purpose
----------------

Computes the Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) stationarity test.

Format
----------------
.. function:: { kpss, cv } = LMkpss(y, model[, bwl, varm])
    :noindexentry:

    :param y: Time series data to be tested.
    :type y: Nx1 matrix

    :param model: Model to be implemented.

        =========== ======================
        1           Constant.
        2           Constant and trend.
        =========== ======================

    :type model: Scalar

    :param bwl: Optional, bandwidth for the spectral window. Default = round(4 * (T/100)^(2/9)).
    :type  bwl: Scalar

    :param varm: 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
        =========== =====================================================

    :type varm: Scalar

    :return kpss: The KPSS test statistic.
    :rtype kpss: Scalar

    :return cvKPSS: 1%, 5%, and 10% critical values for the KPSS test statistic.
    :rtype cvKPSS: Scalar

Examples
--------

::

  library tspdlib;

  // Load date file
  y = loadd(getGAUSSHome() $+ "pkgs/tspdlib/examples/ts_examples.csv", "Y");

  // Constant
  model = 1;
  { kpss, cvKPSS } = lmkpss(y, model);

  // Constant and trend
  model = 2;
  { kpss, cvKPSS } = lmkpss(y, model);


Source
------

kpss.src

.. seealso:: Functions :func:`adf`, :func:`KPSS_1break`, :func:`KPSS_2breaks`, :func:`dfgls`, :func:`erspt`