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# scikits.statsmodels.regression.GLSAR

class `scikits.statsmodels.regression.``GLSAR`(endog, exog=None, rho=1)

  A regression model with an AR(p) covariance structure.

  The linear autoregressive process of order p–AR(p)–is defined as:
    TODO

  Notes

  GLSAR is considered to be experimental.

  Examples

      >>> import scikits.statsmodels as sm
      >>> X = range(1,8)
      >>> X = sm.add_constant(X)
      >>> Y = [1,3,4,5,8,10,9]
      >>> model = sm.GLSAR(Y, X, rho=2)
      >>> for i in range(6):
      ...    results = model.fit()
      ...    print "AR coefficients:", model.rho
      ...    rho, sigma = sm.regression.yule_walker(results.resid,
      ...                 order=model.order)
      ...    model = sm.GLSAR(Y, X, rho)
      AR coefficients: [ 0.  0.]
      AR coefficients: [-0.52571491 -0.84496178]
      AR coefficients: [-0.620642   -0.88654567]
      AR coefficients: [-0.61887622 -0.88137957]
      AR coefficients: [-0.61894058 -0.88152761]
      AR coefficients: [-0.61893842 -0.88152263]
      >>> results.params
      array([ 1.58747943, -0.56145497])
      >>> results.t()
      array([ 30.796394  ,  -2.66543144])
      >>> print results.t_test([0,1])
      <T test: effect=-0.56145497223945595, sd=0.21064318655324663, t=-2.6654314408481032, p=0.022296117189135045, df_denom=5>
      >>> import numpy as np
      >>> print(results.f_test(np.identity(2)))
      <F test: F=2762.4281271616205, p=2.4583312696e-08, df_denom=5, df_num=2>

  Or, equivalently

      >>> model2 = sm.GLSAR(Y, X, rho=2)
      >>> res = model2.iterative_fit(maxiter=6)
      >>> model2.rho
      array([-0.61893842, -0.88152263])

  Methods

  `fit`() | Full fit of the model.
  `hessian`(params) | The Hessian matrix of the model
  `information`(params) | Fisher information matrix of model
  `initialize`() |
  `iterative_fit`([maxiter]) | Perform an iterative two-stage procedure to estimate a GLS model.
  `loglike`(params) | Returns the value of the gaussian loglikelihood function at params.
  `predict`(exog[, params]) | Return linear predicted values from a design matrix.
  `score`(params) | Score vector of model.
  `whiten`(X) | Whiten a series of columns according to an AR(p)

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