class_docstr: Results of fitting an AR(9) model to the sunspot data using exact MLE.
  
  Results were taken from gretl.
code: def __init__(self, constant=True):
      self.avobs = 300
      if constant:
          # NOTE: Stata's estimated parameters differ from gretl
          filename = os.path.join(cur_dir, "ARMLEConstantPredict.csv")
          filename2 = os.path.join(
              cur_dir, "results_ar_forecast_mle_dynamic.csv"
          )
          predictresults = np.loadtxt(filename, delimiter=",")
          pv = predictresults[:, 1]
          dynamicpv = np.genfromtxt(
              filename2, delimiter=",", skip_header=1
          )
  
          # cases - in sample predict
          # start = 0 (fitted values)
          self.FVMLEdefault = pv[:309]
          # start=9
          self.FVMLEstart9end308 = pv[9:309]
          # start=100, end=309
          self.FVMLEstart100end308 = pv[100:309]
          # start = 0, end
          self.FVMLEstart0end200 = pv[:201]
          # n = 200, start = 200
          self.FVMLEstart200end334 = pv[200:]
          # start = 309, end=334 post-sample forecasting
          self.FVMLEstart308end334 = pv[308:]
          # end = 310, start = 9
          self.FVMLEstart9end309 = pv[9:310]
          # end = 301, start = 0
          self.FVMLEstart0end301 = pv[:302]
          # end = 312, start = 4
          self.FVMLEstart4end312 = pv[4:313]
          # end = 7, start = 2
          self.FVMLEstart2end7 = pv[2:8]
  
          self.fcdyn = dynamicpv[:, 0]
          self.fcdyn2 = dynamicpv[:, 1]
          self.fcdyn3 = dynamicpv[:, 2]
          self.fcdyn4 = dynamicpv[:, 3]
  
      else:
          pass
  
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 ARResultsMLE object  with example: obj = ARResultsMLE(constant)