code: def __init__(self, method="mle"):
      if method == "mle":
          # from stata
          from .arima111_results import results
  
          self.__dict__.update(results)
          self.resid = self.resid[1:]
          self.params = self.params[:-1]
          self.sigma2 = self.sigma**2
          self.aic = self.icstats[4]
          self.bic = self.icstats[5]
          self.fittedvalues = self.xb[
              1:
          ]  # no idea why this initial value
          self.linear = self.y[1:]
          self.k_diff = 1
  
          # stata bse are OPG
          # self.bse = np.diag(self.cov_params) ** .5
  
          # from gretl
          self.arroots = [1.027 + 0j, 5.7255 + 0j]
          self.maroots = [1.1442 + 0j]
          self.hqic = 496.5314
          self.aic_gretl = 489.8388
          self.bic_gretl = 506.3801
          self.tvalues = [3.468, 11.14, -1.941, 12.55]
          self.pvalues = [0.0005, 8.14e-29, 0.0522, 3.91e-36]
          cov_params = np.array(
              [
                  [0.0616906, -0.00250187, 0.0010129, 0.00260485],
                  [0, 0.0105302, -0.00867819, -0.00525614],
                  [0, 0, 0.00759185, 0.00361962],
                  [0, 0, 0, 0.00484898],
              ]
          )
          self.cov_params = (
              cov_params + cov_params.T - np.diag(np.diag(cov_params))
          )
          self.bse = np.sqrt(np.diag(self.cov_params))
          # these bse are approx [0.248376, 0.102617, 0.0871312, 0.0696346]
  
          self.forecast = forecast_results["fc211c"][-25:]
          self.forecasterr = forecast_results["fc211cse"][-25:]
          self.forecast_dyn = forecast_results["fc211cdyn"][-25:]
          self.forecasterr_dyn = forecast_results["fc211cdynse"][-25:]
      else:
          from .arima211_css_results import results
  
          self.__dict__.update(results)
          self.resid = self.resid[1:]
          self.params = self.params[:-1]
          self.sigma2 = self.sigma**2
          self.aic = self.icstats[4]
          self.bic = self.icstats[5]
          self.fittedvalues = self.xb[
              1:
          ]  # no idea why this initial value
          self.linear = self.y[1:]
          self.k_diff = 1
  
          # from gretl
          self.arroots = [1.0229 + 0j, 4.4501 + 0j]
          self.maroots = [1.0604 + 0j]
          self.hqic = 489.3225
          self.aic_gretl = 482.6486
          self.bic_gretl = 499.1402
          self.tvalues = [0.7206, 22.54, -19.04]
          self.pvalues = [0.4712, 1.52e-112, 2.19e-10, 8.00e-81]
          cov_params = np.array(
              [
                  [8.20496e-04, -0.0011992, 4.57078e-04, 0.00109907],
                  [0, 0.00284432, -0.0016752, -0.00220223],
                  [0, 0, 0.00119783, 0.00108868],
                  [0, 0, 0, 0.00245324],
              ]
          )
          self.cov_params = (
              cov_params + cov_params.T - np.diag(np.diag(cov_params))
          )
          self.bse = np.sqrt(np.diag(self.cov_params))
          # forecasting is not any different for css
          # except you lose the first p+1 observations for in-sample
          self.forecast = forecast_results["fc111c_css"][-25:]
          self.forecasterr = forecast_results["fc111cse_css"][-25:]
          self.forecast_dyn = forecast_results["fc111cdyn_css"]
          self.forecasterr_dyn = forecast_results["fc111cdynse_css"]
  
file_path: statsmodels/statsmodels/tsa/tests/results/results_arima.py
imports: import os
  import numpy
  from numpy import genfromtxt
label_desc: Use image node_id 1 to create a new ARIMA211 object  with example: obj = ARIMA211(method)