code: def __init__(self):
      params = [
          -0.2794863875,
          0.0082427826,
          0.6750534746,
          0.2904420695,
          0.0332267098,
          -0.0073250059,
          0.0015269951,
          -0.1004938623,
          -0.1231841792,
          0.2686635768,
          0.2325045441,
          0.0257430635,
          0.0235035714,
          0.0054596064,
          -1.97116e00,
          0.3809752365,
          4.4143364022,
          0.8001168377,
          0.2255078864,
          -0.1241109271,
          -0.0239026118,
      ]
      params = np.asarray(params).reshape(3, -1)
      params = np.hstack(
          (params[:, -1][:, None], params[:, :-1:2], params[:, 1::2])
      )
      self.params = params
      self.neqs = 3
      self.nobs = 200
      self.df_eq = 7
      self.nobs_1 = 200
      self.df_model_1 = 6
      self.rmse_1 = 0.0075573716985351
      self.rsquared_1 = 0.2739094844780006
      self.llf_1 = 696.8213727557811
      self.nobs_2 = 200
      self.rmse_2 = 0.0065444260782597
      self.rsquared_2 = 0.1423626064753714
      self.llf_2 = 725.6033255319256
      self.nobs_3 = 200
      self.rmse_3 = 0.0395942039671031
      self.rsquared_3 = 0.2955406949737428
      self.llf_3 = 365.5895183036045
      # These are from Stata.  They use the LL based definition
      # We return Lutkepohl statistics.  See Stata TS manual page 436
      #        self.bic = -19.06939794312953
      #        self.aic = -19.41572126661708
      #        self.hqic = -19.27556951526737
      # These are from R.  See var.R in macrodata folder
      self.bic = -2.758301611618373e01
      self.aic = -2.792933943967127e01
      self.hqic = -2.778918768832157e01
      self.fpe = 7.421287668357018e-13
      self.detsig = 6.01498432283e-13
      self.llf = 1962.572126661708
  
      self.chi2_1 = 75.44775165699033
      # do not know how they calculate this;  it's not -2 * (ll1 - ll0)
  
      self.chi2_2 = 33.19878716815366
      self.chi2_3 = 83.90568280242312
      bse = [
          0.1666662376,
          0.1704584393,
          0.1289691456,
          0.1433308696,
          0.0257313781,
          0.0253307796,
          0.0010992645,
          0.1443272761,
          0.1476111934,
          0.1116828804,
          0.1241196435,
          0.0222824956,
          0.021935591,
          0.0009519255,
          0.8731894193,
          0.8930573331,
          0.6756886998,
          0.7509319263,
          0.1348105496,
          0.1327117543,
          0.0057592114,
      ]
      bse = np.asarray(bse).reshape(3, -1)
      bse = np.hstack(
          (bse[:, -1][:, None], bse[:, :-1:2], bse[:, 1::2])
      )
      self.bse = bse
  
label_desc: Use image node_id 1 to create a new MacrodataResults object  with example: obj = MacrodataResults()