class_docstr: Results of fitting an AR(9) model to the sunspot data.
  
  Results were taken from Stata using the var command.
code: def __init__(self, constant=True):
      self.avobs = 300.0
      if constant:
          self.params = [
              6.7430535917332,
              1.1649421971129,
              -0.40535742259304,
              -0.16653934246587,
              0.14980629416032,
              -0.09462417064796,
              0.00491001240749,
              0.0504665930841,
              -0.08635349190816,
              0.25349103194757,
          ]
          # These are returned by stata VAR, using the (V)AR scale/sigma
          # we return the true OLS bse by default
          # the stata residuals can be achieved
          # by np.sqrt(np.diag(res1.cov_params()))
          self.bse_stata = [
              2.413485601,
              0.0560359041,
              0.0874490762,
              0.0900894414,
              0.0899348339,
              0.0900100797,
              0.0898385666,
              0.0896997939,
              0.0869773089,
              0.0559505756,
          ]
          # The below are grom gretl's ARIMA command with conditional
          # maximum likelihood
          self.bse_gretl = [
              2.45474,
              0.0569939,
              0.0889440,
              0.0916295,
              0.0914723,
              0.0915488,
              0.0913744,
              0.0912332,
              0.0884642,
              0.0569071,
          ]
          self.rmse = 15.1279294937327
          self.fpe = 236.4827257929261
          self.llf = -1235.559128419549
          # NOTE: we use a different definition of these ic than Stata
          # but our order selection results agree with R VARselect
          # close to Stata for Lutkepohl but we penalize the ic for
          # the trend terms
          # self.bic = 8.427186938618863
          # self.aic = 8.30372752279699
          # self.hqic = 8.353136159250697
  
          # NOTE: predictions were taken from gretl, but agree with Stata
          #   test predict
          # TODO: remove one of the files
          filename = os.path.join(cur_dir, "AROLSConstantPredict.csv")
          predictresults = np.loadtxt(filename)
          fv = predictresults[:300, 0]
          pv = predictresults[300:, 1]
          del predictresults
  
          # cases - in sample predict
          # n = -1, start = 0 (fitted values)
          self.FVOLSnneg1start0 = fv
          # n=-1, start=9
          self.FVOLSnneg1start9 = fv
          # n=-1, start=100
          self.FVOLSnneg1start100 = fv[100 - 9 :]
          # n = 200, start = 0
          self.FVOLSn200start0 = fv[:192]
          # n = 200, start = 200
          self.FVOLSn200start200 = np.hstack(
              (fv[200 - 9 :], pv[: 101 - 9])
          )
          # n = 200, start = -109 use above
          self.FVOLSn200startneg109 = self.FVOLSn200start200
          # n = 100, start = 325, post-sample forecasting
          self.FVOLSn100start325 = np.hstack((fv[-1], pv))
          # n = 301, start = 9
          self.FVOLSn301start9 = np.hstack((fv, pv[:2]))
          # n = 301, start = 0
          self.FVOLSdefault = fv
          # n = 4, start = 312
          self.FVOLSn4start312 = np.hstack((fv[-1], pv[:8]))
          # n = 15, start = 312
          self.FVOLSn15start312 = np.hstack((fv[-1], pv[:19]))
  
      elif not constant:
          self.params = [
              1.19582389902985,
              -0.40591818219637,
              -0.15813796884843,
              0.16620079925202,
              -0.08570200254617,
              0.01876298948686,
              0.06130211910707,
              -0.08461507700047,
              0.27995084653313,
          ]
          self.bse_stata = [
              0.055645055,
              0.088579237,
              0.0912031179,
              0.0909032462,
              0.0911161784,
              0.0908611473,
              0.0907743174,
              0.0880993504,
              0.0558560278,
          ]
          self.bse_gretl = [
              0.0564990,
              0.0899386,
              0.0926027,
              0.0922983,
              0.0925145,
              0.0922555,
              0.0921674,
              0.0894513,
              0.0567132,
          ]
          self.rmse = 15.29712618677774
          self.sigma = 226.9820074869752
          self.llf = -1239.41217278661
          # See note above; TODO: be more specific about "above"
          # self.bic = 8.433861292817106
          # self.hqic = 8.367215591385756
          # self.aic = 8.322747818577421
          self.fpe = 241.0221316614273
  
          filename = os.path.join(cur_dir, "AROLSNoConstantPredict.csv")
          predictresults = np.loadtxt(filename)
          fv = predictresults[:300, 0]
          pv = predictresults[300:, 1]
          del predictresults
  
          # cases - in sample predict
          # n = -1, start = 0 (fitted values)
          self.FVOLSnneg1start0 = fv
          # n=-1, start=9
          self.FVOLSnneg1start9 = fv
          # n=-1, start=100
          self.FVOLSnneg1start100 = fv[100 - 9 :]
          # n = 200, start = 0
          self.FVOLSn200start0 = fv[:192]
          # n = 200, start = 200
          self.FVOLSn200start200 = np.hstack(
              (fv[200 - 9 :], pv[: 101 - 9])
          )
          # n = 200, start = -109 use above
          self.FVOLSn200startneg109 = self.FVOLSn200start200
          # n = 100, start = 325, post-sample forecasting
          self.FVOLSn100start325 = np.hstack((fv[-1], pv))
          # n = 301, start = 9
          self.FVOLSn301start9 = np.hstack((fv, pv[:2]))
          # n = 301, start = 0
          self.FVOLSdefault = fv
          # n = 4, start = 312
          self.FVOLSn4start312 = np.hstack((fv[-1], pv[:8]))
          # n = 15, start = 312
          self.FVOLSn15start312 = np.hstack((fv[-1], pv[:19]))
  
file_path: statsmodels/statsmodels/tsa/tests/results/results_ar.py
imports: import os
  import numpy
label_desc: Use image node_id 1 to create a new ARResultsOLS object  with example: obj = ARResultsOLS(constant)