class_docstr: The results were generated from Bruce Hansen's
  MATLAb package:
  
  Bruce E. Hansen
  Department of Economics
  Social Science Building
  University of Wisconsin
  Madison, WI 53706-1393
  bhansen@ssc.wisc.edu
  http://www.ssc.wisc.edu/~bhansen/
  
  The R results are from Mai Zhou's emplik package
class_docstr_tok: ["The","results","were","generated","from","Bruce","Hansen's","MATLAb","package",":","Bruce","E.","Hansen","Department","of","Economics","Social","Science","Building","University","of","Wisconsin","Madison",",","WI","53706-1393","bhansen","@","ssc.wisc.edu","http",":","\/\/www.ssc.wisc.edu\/~bhansen\/","The","R","results","are","from","Mai","Zhou","'s","emplik","package"]
code: def __init__(self):
      self.ci_mean = (13.556709, 14.559394)
      self.test_mean_14 = (0.080675, 0.776385)
      self.test_mean_weights = np.array(
          [
              [0.01969213],
              [0.01911859],
              [0.01973982],
              [0.01982913],
              [0.02004183],
              [0.0195765],
              [0.01970423],
              [0.02015074],
              [0.01898431],
              [0.02067787],
              [0.01878104],
              [0.01920531],
              [0.01981207],
              [0.02031582],
              [0.01857329],
              [0.01907883],
              [0.01943674],
              [0.0210042],
              [0.0197373],
              [0.01997998],
              [0.0199233],
              [0.01986713],
              [0.02017751],
              [0.01962176],
              [0.0214596],
              [0.02118228],
              [0.02013767],
              [0.01918665],
              [0.01908886],
              [0.01943081],
              [0.01916251],
              [0.01868129],
              [0.01918334],
              [0.01969084],
              [0.01984322],
              [0.0198977],
              [0.02098504],
              [0.02132222],
              [0.02100581],
              [0.01970351],
              [0.01942184],
              [0.01979781],
              [0.02114276],
              [0.02096136],
              [0.01999804],
              [0.02044712],
              [0.02174404],
              [0.02189933],
              [0.02077078],
              [0.02082612],
          ]
      ).squeeze()
      self.test_var_3 = (0.199385, 0.655218)
      self.ci_var = (2.290077, 4.423634)
      self.test_var_weights = np.array(
          [
              [0.020965],
              [0.019686],
              [0.021011],
              [0.021073],
              [0.021089],
              [0.020813],
              [0.020977],
              [0.021028],
              [0.019213],
              [0.02017],
              [0.018397],
              [0.01996],
              [0.021064],
              [0.020854],
              [0.017463],
              [0.019552],
              [0.020555],
              [0.019283],
              [0.021009],
              [0.021103],
              [0.021102],
              [0.021089],
              [0.021007],
              [0.020879],
              [0.017796],
              [0.018726],
              [0.021038],
              [0.019903],
              [0.019587],
              [0.020543],
              [0.019828],
              [0.017959],
              [0.019893],
              [0.020963],
              [0.02108],
              [0.021098],
              [0.01934],
              [0.018264],
              [0.019278],
              [0.020977],
              [0.020523],
              [0.021055],
              [0.018853],
              [0.019411],
              [0.0211],
              [0.02065],
              [0.016803],
              [0.016259],
              [0.019939],
              [0.019793],
          ]
      ).squeeze()
      self.mv_test_mean = (0.7002663, 0.7045943)
      self.mv_test_mean_wts = np.array(
          [
              [0.01877015],
              [0.01895746],
              [0.01817092],
              [0.01904308],
              [0.01707106],
              [0.01602806],
              [0.0194296],
              [0.01692204],
              [0.01978322],
              [0.01881313],
              [0.02011785],
              [0.0226274],
              [0.01953733],
              [0.01874346],
              [0.01694224],
              [0.01611816],
              [0.02297437],
              [0.01943187],
              [0.01899873],
              [0.02113375],
              [0.02295293],
              [0.02043509],
              [0.02276583],
              [0.02208274],
              [0.02466621],
              [0.02287983],
              [0.0173136],
              [0.01905693],
              [0.01909335],
              [0.01982534],
              [0.01924093],
              [0.0179352],
              [0.01871907],
              [0.01916633],
              [0.02022359],
              [0.02228696],
              [0.02080555],
              [0.01725214],
              [0.02166185],
              [0.01798537],
              [0.02103582],
              [0.02052757],
              [0.03096074],
              [0.01966538],
              [0.02201629],
              [0.02094854],
              [0.02127771],
              [0.01961964],
              [0.02023756],
              [0.01774807],
          ]
      ).squeeze()
      self.test_skew = (2.498418, 0.113961)
      self.test_skew_wts = np.array(
          [
              [0.016698],
              [0.01564],
              [0.01701],
              [0.017675],
              [0.019673],
              [0.016071],
              [0.016774],
              [0.020902],
              [0.016397],
              [0.027359],
              [0.019136],
              [0.015419],
              [0.01754],
              [0.022965],
              [0.027203],
              [0.015805],
              [0.015565],
              [0.028518],
              [0.016992],
              [0.019034],
              [0.018489],
              [0.01799],
              [0.021222],
              [0.016294],
              [0.022725],
              [0.027133],
              [0.020748],
              [0.015452],
              [0.015759],
              [0.01555],
              [0.015506],
              [0.021863],
              [0.015459],
              [0.01669],
              [0.017789],
              [0.018257],
              [0.028578],
              [0.025151],
              [0.028512],
              [0.01677],
              [0.015529],
              [0.01743],
              [0.027563],
              [0.028629],
              [0.019216],
              [0.024677],
              [0.017376],
              [0.014739],
              [0.028112],
              [0.02842],
          ]
      ).squeeze()
      self.test_kurt_0 = (1.904269, 0.167601)
      self.test_corr = (
          0.012025,
          0.912680,
      )
      self.test_corr_weights = np.array(
          [
              [0.020037],
              [0.020108],
              [0.020024],
              [0.02001],
              [0.019766],
              [0.019971],
              [0.020013],
              [0.019663],
              [0.019944],
              [0.01982],
              [0.01983],
              [0.019436],
              [0.020005],
              [0.019897],
              [0.020768],
              [0.020468],
              [0.019521],
              [0.019891],
              [0.020024],
              [0.01997],
              [0.019824],
              [0.019976],
              [0.019979],
              [0.019753],
              [0.020814],
              [0.020474],
              [0.019751],
              [0.020085],
              [0.020087],
              [0.019977],
              [0.020057],
              [0.020435],
              [0.020137],
              [0.020025],
              [0.019982],
              [0.019866],
              [0.020151],
              [0.019046],
              [0.020272],
              [0.020034],
              [0.019813],
              [0.01996],
              [0.020657],
              [0.019947],
              [0.019931],
              [0.02008],
              [0.02035],
              [0.019823],
              [0.02005],
              [0.019497],
          ]
      ).squeeze()
      self.test_joint_skew_kurt = (8.753952, 0.012563)
  
file_path: statsmodels/statsmodels/emplike/tests/results/el_results.py
imports: import numpy
label_desc: Use image node_id 1 to create a new DescStatRes object  with example: obj = DescStatRes()