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alkaline-ml / statsmodels python
Repository URL to install this package:
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Version:
0.11.1 ▾
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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):
"""
Results for the AFT model from package emplik in R written by Mai Zhou
"""
def __init__(self):
self.test_params = np.array([3.77710799, -0.03281745])
self.test_beta0 = (.132511, 0.7158323)
self.test_beta1 = (.297951, .5851693)
self.test_joint = (11.8068, 0.002730147)
class OriginResults(object):
"""
These results are from Bruce Hansen's Matlab package.
To replicate the results, the exogenous variables were scaled
down by 10**-2 and the results were then rescaled.
These tests must also test likelihood functions because the
llr when conducting hypothesis tests is the MLE while
restricting the intercept to 0. Matlab's generic package always
uses the unrestricted MLE.
"""
def __init__(self):
self.test_params = np.array([0, .00351861])
self.test_llf = -1719.793173 # llf when testing param = .0034
self.test_llf_hat = -1717.95833 # llf when origin=0
self.test_llf_hypoth = -2*(self.test_llf-self.test_llf_hat)
self.test_llf_conf = -1719.879077 # The likelihood func at conf_vals