code: def __init__(self, method="mle"):
      if method == "mle":
          self.params = [
              0.763798613302,
              0.306453049063,
              -0.835653786888,
              0.151382611965,
              0.421169903784,
          ]
          self.aic = 736.001094752429
          self.bic = 757.129860259603
          self.arroots = 1.3092 + 0j
          self.maroots = [
              1.0392 - 0.7070j,
              1.0392 + 0.7070j,
              -1.2189 - 0.1310j,
              -1.2189 + 0.1310j,
          ]
          self.bse = [
              0.064888368113,
              0.078031359430,
              0.076246826219,
              0.069267771804,
              0.071567389557,
          ]
          self.cov_params = [
              [
                  0.0042105,
                  -0.0031074,
                  -0.0027947,
                  -0.00027766,
                  -0.00037373,
              ],
              [
                  -0.0031074,
                  0.0060889,
                  0.0033958,
                  -0.0026825,
                  -0.00062289,
              ],
              [
                  -0.0027947,
                  0.0033958,
                  0.0058136,
                  -0.00063747,
                  -0.0028984,
              ],
              [
                  -0.00027766,
                  -0.0026825,
                  -0.00063747,
                  0.0047980,
                  0.0026998,
              ],
              [
                  -0.00037373,
                  -0.00062289,
                  -0.0028984,
                  0.0026998,
                  0.0051219,
              ],
          ]
          self.hqic = 744.504804564101
          self.llf = -362.000547376215
          self.resid = resids_mle[:, 1]
          self.fittedvalues = yhat_mle[:, 1]
          self.pvalues = [
              5.51e-32,
              8.59e-05,
              5.96e-28,
              0.0289,
              3.98e-09,
          ]
          self.tvalues = [11.77, 3.927, -10.96, 2.185, 5.885]
          self.sigma2 = 1.022607088673**2
          self.bse = [
              0.064888368113,
              0.078031359430,
              0.076246826219,
              0.069267771804,
              0.071567389557,
          ]
      elif method == "css":
          self.params = [
              0.772072791055,
              0.283961556581,
              -0.834797380642,
              0.157773469382,
              0.431616426021,
          ]
          self.aic = 734.294057687460
          self.bic = 755.398775066249
          self.arroots = [1.2952 + 0.0000j]
          self.maroots = [
              1.0280 - 0.6987j,
              1.0280 + 0.6987j,
              -1.2108 - 0.1835j,
              -1.2108 + 0.1835j,
          ]
          # NOTE: bse, cov_params, tvalues taken from R; commented-out
          #   versions below are from [TODO: finish this sentence]
          # self.bse = [0.083423762397, 0.086852297123, 0.093883465705,
          #             0.068170451942, 0.065938183073]
          self.bse = [
              0.06106330,
              0.07381130,
              0.07257705,
              0.06857992,
              0.07046048,
          ]
          # self.cov_params = [
          #   [0.0069595, -0.0053083, -0.0054522, -0.0016324, -0.00099984],
          #   [-0.0053083, 0.0075433, 0.0052442, -0.00071680, 0.0010335],
          #   [-0.0054522, 0.0052442, 0.0088141, 0.0019754, -0.0018231],
          #   [-0.0016324, -0.00071680, 0.0019754, 0.0046472, 0.0011853],
          #   [-0.00099984, 0.0010335, -0.0018231, 0.0011853, 0.0043478]]
          self.cov_params = [
              [
                  0.0037287270,
                  -0.0025337305,
                  -0.0023475489,
                  -0.0001894180,
                  -0.0002716368,
              ],
              [
                  -0.0025337305,
                  0.0054481087,
                  0.0029356374,
                  -0.0027307668,
                  -0.0008073432,
              ],
              [
                  -0.0023475489,
                  0.0029356374,
                  0.0052674275,
                  -0.0007578638,
                  -0.0028534882,
              ],
              [
                  -0.0001894180,
                  -0.0027307668,
                  -0.0007578638,
                  0.0047032056,
                  0.0026710177,
              ],
              [
                  -0.0002716368,
                  -0.0008073432,
                  -0.0028534882,
                  0.0026710177,
                  0.0049646795,
              ],
          ]
          self.hqic = 742.789053551421
          self.llf = -361.147028843730
          self.resid = resids_css[1:, 1]
          self.fittedvalues = yhat_css[1:, 1]
          self.pvalues = [2.15e-20, 0.0011, 6.01e-19, 0.0206, 5.92e-11]
          # self.tvalues = [9.255, 3.269, -8.892, 2.314, 6.546]
          self.tvalues = [
              12.643194,
              3.847252,
              -11.501785,
              2.301399,
              6.126120,
          ]
          self.sigma2 = 1.031950951582**2
  
imports: import os
  from numpy import genfromtxt
label_desc: Use image node_id 1 to create a new Y_arma14 object  with example: obj = Y_arma14(method)