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"""
Results from Matlab and R
"""
import numpy as np
class DescStatRes(object):
"""
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
"""
def __init__(self):
self.ci_mean = (13.556709, 14.559394)
self.test_mean_14 = (.080675, .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 = (.199385, .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 = (.7002663, .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, .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, .167601)
self.test_corr = (.012025, .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, .012563)
class RegressionResults(object):
"""
Results for EL Regression
"""
def __init__(self):
self.source = 'Matlab'
self.test_beta0 = (1.758104, .184961, np.array([
0.04326392, 0.04736749, 0.03573865, 0.04770535, 0.04721684,
0.04718301, 0.07088816, 0.05631242, 0.04865098, 0.06572099,
0.04016013, 0.04438627, 0.04042288, 0.03938043, 0.04006474,
0.04845233, 0.01991985, 0.03623254, 0.03617999, 0.05607242,
0.0886806]))
self.test_beta1 = (1.932529, .164482, np.array([
0.033328, 0.051412, 0.03395, 0.071695, 0.046433, 0.041303,
0.033329, 0.036413, 0.03246, 0.037776, 0.043872, 0.037507,
0.04762, 0.04881, 0.05874, 0.049553, 0.048898, 0.04512,
0.041142, 0.048121, 0.11252]))
self.test_beta2 = (.494593, .481866, np.array([
0.046287, 0.048632, 0.048772, 0.034769, 0.048416, 0.052447,
0.053336, 0.050112, 0.056053, 0.049318, 0.053609, 0.055634,
0.042188, 0.046519, 0.048415, 0.047897, 0.048673, 0.047695,
0.047503, 0.047447, 0.026279]))
self.test_beta3 = (3.537250, .060005, np.array([
0.036327, 0.070483, 0.048965, 0.087399, 0.041685, 0.036221,
0.016752, 0.019585, 0.027467, 0.02957, 0.069204, 0.060167,
0.060189, 0.030007, 0.067371, 0.046862, 0.069814, 0.053041,
0.053362, 0.041585, 0.033943]))
self.test_ci_beta0 = (-52.77128837058528, -24.11607348661947)
self.test_ci_beta1 = (0.41969831751229664, 0.9857167306604057)
self.test_ci_beta2 = (0.6012045929381431, 2.1847079367275692)
self.test_ci_beta3 = (-0.3804313225443794, 0.006934528877337928)
class ANOVAResults(object):
"""
Results for ANOVA
"""
def __init__(self):
self.source = 'Matlab'
self.compute_ANOVA = (.426163, .51387, np.array([9.582371]), np.array([
0.018494, 0.01943, 0.016624, 0.0172, 0.016985, 0.01922,
0.016532, 0.015985, 0.016769, 0.017631, 0.017677, 0.017984,
0.017049, 0.016721, 0.016382, 0.016566, 0.015642, 0.015894,
0.016282, 0.015704, 0.016272, 0.015678, 0.015651, 0.015648,
0.015618, 0.015726, 0.015981, 0.01635, 0.01586, 0.016443,
0.016126, 0.01683, 0.01348, 0.017391, 0.011225, 0.017282,
0.015568, 0.017543, 0.017009, 0.016325, 0.012841, 0.017648,
0.01558, 0.015994, 0.017258, 0.017664, 0.017792, 0.017772,
0.017527, 0.017797, 0.017856, 0.017849, 0.017749, 0.017827,
0.017381, 0.017902, 0.016557, 0.015522, 0.017455, 0.017248]))
class AFTRes(object):