garchGJRFit
===========

Purpose
-------
Estimates asymmetric GJR-GARCH model.

Library
-------
tsmt

Format
------
.. function:: out1 = garchGJRFit(y, p [, q, c0]);
              out1 = garchGJRFit(dataset, formula, p [, q, c0]);


      :param y: dependent variables.
      :type y: Matrix

      :param x: independent variables.
      :type x: Matrix

      :param dataset: name of data set or null string.
      :type dataset: string

      :param formula: formula string of the model. E.g. ``"y ~ X1 + X2"`` 'y' is the name of dependent variable, '``X1``' and '``X2``' are names of independent variables; E.g. ``"y ~ ."`` , '.' means including all variables except dependent variable 'y';
      :type formula: string

      :param p: order of the GARCH parameters.
      :type p: scalar

      :param q: Optional input. order of the ARCH parameters.
      :type q: scalar

      :param c0: Optional input. :class:`garchControl` structure.

         .. list-table::
            :widths: auto

            * - c0.density
               - scalar, density of error term:

                 :0: Normal
                 :1: Student's t
                 :3: skew generalized t.

           * - c0.asymmetry
             - scalar, if nonzero assymetry terms are added.
           * - c0.inmean
             - scalar, GARCH-in-mean, square root of conditional variance is included in the mean equation.
           * - c0.stConstraintsType
             - scalar, type of enforcement of stationarity requirements:

                 :1: roots of characteristic polynomial constrained outside unit circle
                 :2: arch, GARCH parameters constrained to sum to less than one and greater than zero
                 :3: none.

           * - c0.cvConstraintsType
             - scalar, type of enforcement of nonnegative conditional variances:

                 :0: direct constraints
                 :1: Nelson & Cao constraints

          * - c0.covType
            - scalar, type of covariance matrix of parameters:

                 :1: ML
                 :2: QML
                 :3: none

       :type c0: Optional input

       :return out1: garchEstimation structure containing the following members:

          .. list-table::
            :widths: auto

            * - out1.aic
              - scalar, Akiake criterion.
            * - out1.bic
              - scalar, Bayesian information criterion.
            * - out1.lrs
              - scalar, likelihood ratio statistic.
            * - out1.numObs
              - scalar, number of observations.
            * - out1.df
              - scalar, degrees of freedom.
            * - out1.par
              - instance of PV structure containing parameter estimates.
            * - out1.retcode
              - scalar, return code:

                  :1: normal convergence.
                  :2: forced exit.
                  :3: function calculation failed.
                  :4: gradient calculation failed.
                  :5: Hessian calculation failed.
                  :6: line search failed.
                  :7: error with constraints.
                  :8: function complex.

            * - out1.moment
              - KxK matrix, moment matrix of parameter estimates.
            * - out1.climits
              - Kx2 matrix, confidence limits.

        :rtype out1: struct


Example
-------

::

  new;
  cls,;
  library tsmt;

  y = loadd( getGAUSSHome() $+ "pkgs/tsmt/examples/gjrgarch.dat");

  // GARCH control structure
  struct garchControl c0;
  c0 = garchControlCreate;
  c0.cmlmtControlproc = &prc;

  // Covariance type
  c0.covtype = 2;


  // Control cmlmt estimation
  proc prc(struct cmlmtControl c0);
      c0.printiters = 10;
      c0.switch = 0;
      c0.algorithm = 1;
  retp(c0);
  endp;

  // GARCH order
  p = 1;

  // ARCH order
  q = 1;

  // Estimate model
  struct garchEstimation f0;
  f0 = garchgjrFit(y, p, q, c0);


Source
------
tsgarch.src

.. seealso:: Functions :func:`garchFit`, :func:`garchMFit`