code: def __init__(self, method="mle"):
      if method == "mle":
          self.params = [
              0.726892679311,
              -0.312619864536,
              0.323740181610,
              0.226499145083,
              -0.089562902305,
          ]
          self.aic = 691.422630427314
          self.bic = 712.551395934487
          self.arroots = [
              1.0772 + 0.0000j,
              0.0087 - 1.2400j,
              0.0087 + 1.2400j,
              -1.9764 + 0.0000j,
              3.4107 + 0.0000j,
          ]
          self.maroots = None  # TODO: empty array?
          self.bse = [
              0.062942787895,
              0.076539691571,
              0.076608230545,
              0.077330717503,
              0.063499540628,
          ]
          self.cov_params = [
              [
                  0.0039618,
                  -0.0028252,
                  0.0013351,
                  -0.0013901,
                  -0.00066624,
              ],
              [
                  -0.0028252,
                  0.0058583,
                  -0.0040200,
                  0.0026059,
                  -0.0014275,
              ],
              [0.0013351, -0.0040200, 0.0058688, -0.0041018, 0.0013917],
              [
                  -0.0013901,
                  0.0026059,
                  -0.0041018,
                  0.0059800,
                  -0.0028959,
              ],
              [
                  -0.00066624,
                  -0.0014275,
                  0.0013917,
                  -0.0028959,
                  0.0040322,
              ],
          ]
          self.hqic = 699.926340238986
          self.llf = -339.711315213657
          self.resid = resids_mle[:, 4]
          self.fittedvalues = yhat_mle[:, 4]
          self.pvalues = [7.51e-31, 4.42e-05, 2.38e-05, 0.0034, 0.1584]
          self.tvalues = [11.55, -4.084, 4.226, 2.929, -1.410]
          self.sigma2 = 0.938374940397**2
          self.forecast = forecast_results["fc50"]
          self.forecasterr = forecast_results["fe50"]
      elif method == "css":
          # NOTE: some results use x-12 arima because gretl uses
          #   LS estimates for AR CSS
          self.params = [
              0.725706505843,
              -0.305501865989,
              0.320719417706,
              0.226552951649,
              -0.089852608091,
          ]
          # self.aic = 674.817286564674
          self.aic = 676.8173
          # self.bic = 692.323577617397
          self.bic = 697.8248
          self.arroots = [
              1.0755 + 0.0000j,
              0.0075 - 1.2434j,
              0.0075 + 1.2434j,
              -1.9686 + 0.0000j,
              3.3994 + 0.0000j,
          ]
          self.maroots = None
          self.bse = [
              0.064344956583,
              0.078060866211,
              0.077980166982,
              0.078390791831,
              0.064384559496,
          ]
          self.cov_params = [
              [
                  0.0041403,
                  -0.0029335,
                  0.0013775,
                  -0.0014298,
                  -0.00068813,
              ],
              [
                  -0.0029335,
                  0.0060935,
                  -0.0041786,
                  0.0026980,
                  -0.0014765,
              ],
              [0.0013775, -0.0041786, 0.0060809, -0.0042177, 0.0014572],
              [
                  -0.0014298,
                  0.0026980,
                  -0.0042177,
                  0.0061451,
                  -0.0029853,
              ],
              [
                  -0.00068813,
                  -0.0014765,
                  0.0014572,
                  -0.0029853,
                  0.0041454,
              ],
          ]
          # self.hqic = 681.867054880965
          self.hqic = 685.2770
          self.llf = -332.408643282337
          self.resid = resids_css[5:, 4]
          self.fittedvalues = yhat_css[5:, 4]
          self.pvalues = [1.68e-29, 9.09e-05, 3.91e-05, 0.0039, 0.1628]
          self.tvalues = [11.28, -3.914, 4.113, 2.890, -1.396]
          # self.sigma2 = 0.949462810435**2
          self.sigma2 = 0.939724**2
  
imports: import os
  from numpy import genfromtxt
label_desc: Use image node_id 1 to create a new Y_arma50 object  with example: obj = Y_arma50(method)