reconnect moved files to git repo
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# Init for results
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"""
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Results from Matlab and R
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"""
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import numpy as np
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class DescStatRes:
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"""
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The results were generated from Bruce Hansen's
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MATLAb package:
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Bruce E. Hansen
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Department of Economics
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Social Science Building
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University of Wisconsin
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Madison, WI 53706-1393
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bhansen@ssc.wisc.edu
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http://www.ssc.wisc.edu/~bhansen/
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The R results are from Mai Zhou's emplik package
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"""
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def __init__(self):
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self.ci_mean = (13.556709, 14.559394)
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self.test_mean_14 = (.080675, .776385)
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self.test_mean_weights = np.array([[0.01969213],
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[0.01911859],
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[0.01973982],
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[0.01982913],
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[0.02004183],
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[0.0195765],
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[0.01970423],
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[0.02015074],
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[0.01898431],
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[0.02067787],
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[0.01878104],
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[0.01920531],
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[0.01981207],
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[0.02031582],
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[0.01857329],
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[0.01907883],
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[0.01943674],
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[0.0210042],
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[0.0197373],
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[0.01997998],
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[0.0199233],
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[0.01986713],
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[0.02017751],
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[0.01962176],
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[0.0214596],
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[0.02118228],
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[0.02013767],
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[0.01918665],
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[0.01908886],
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[0.01943081],
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[0.01916251],
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[0.01868129],
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[0.01918334],
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[0.01969084],
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[0.01984322],
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[0.0198977],
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[0.02098504],
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[0.02132222],
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[0.02100581],
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[0.01970351],
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[0.01942184],
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[0.01979781],
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[0.02114276],
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[0.02096136],
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[0.01999804],
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[0.02044712],
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[0.02174404],
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[0.02189933],
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[0.02077078],
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[0.02082612]]).squeeze()
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self.test_var_3 = (.199385, .655218)
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self.ci_var = (2.290077, 4.423634)
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self.test_var_weights = np.array([[0.020965],
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[0.019686],
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[0.021011],
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[0.021073],
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[0.021089],
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[0.020813],
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[0.020977],
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[0.021028],
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[0.019213],
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[0.02017],
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[0.018397],
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[0.01996],
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[0.021064],
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[0.020854],
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[0.017463],
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[0.019552],
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[0.020555],
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[0.019283],
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[0.021009],
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[0.021103],
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[0.021102],
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[0.021089],
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[0.021007],
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[0.020879],
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[0.017796],
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[0.018726],
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[0.021038],
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[0.019903],
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[0.019587],
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[0.020543],
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[0.019828],
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[0.017959],
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[0.019893],
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[0.020963],
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[0.02108],
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[0.021098],
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[0.01934],
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[0.018264],
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[0.019278],
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[0.020977],
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[0.020523],
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[0.021055],
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[0.018853],
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[0.019411],
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[0.0211],
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[0.02065],
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[0.016803],
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[0.016259],
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[0.019939],
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[0.019793]]).squeeze()
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self.mv_test_mean = (.7002663, .7045943)
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self.mv_test_mean_wts = np.array([[0.01877015],
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[0.01895746],
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[0.01817092],
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[0.01904308],
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[0.01707106],
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[0.01602806],
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[0.0194296],
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[0.01692204],
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[0.01978322],
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[0.01881313],
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[0.02011785],
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[0.0226274],
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[0.01953733],
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[0.01874346],
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[0.01694224],
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[0.01611816],
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[0.02297437],
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[0.01943187],
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[0.01899873],
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[0.02113375],
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[0.02295293],
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[0.02043509],
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[0.02276583],
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[0.02208274],
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[0.02466621],
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[0.02287983],
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[0.0173136],
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[0.01905693],
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[0.01909335],
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[0.01982534],
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[0.01924093],
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[0.0179352],
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[0.01871907],
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[0.01916633],
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[0.02022359],
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[0.02228696],
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[0.02080555],
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[0.01725214],
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[0.02166185],
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[0.01798537],
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[0.02103582],
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[0.02052757],
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[0.03096074],
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[0.01966538],
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[0.02201629],
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[0.02094854],
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[0.02127771],
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[0.01961964],
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[0.02023756],
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[0.01774807]]).squeeze()
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self.test_skew = (2.498418, .113961)
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self.test_skew_wts = np.array([[0.016698],
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[0.01564],
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[0.01701],
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[0.017675],
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[0.019673],
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[0.016071],
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[0.016774],
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[0.020902],
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[0.016397],
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[0.027359],
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[0.019136],
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[0.015419],
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[0.01754],
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[0.022965],
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[0.027203],
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[0.015805],
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[0.015565],
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[0.028518],
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[0.016992],
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[0.019034],
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[0.018489],
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[0.01799],
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[0.021222],
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[0.016294],
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[0.022725],
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[0.027133],
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[0.020748],
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[0.015452],
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[0.015759],
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[0.01555],
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[0.015506],
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[0.021863],
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[0.015459],
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[0.01669],
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[0.017789],
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[0.018257],
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[0.028578],
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[0.025151],
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[0.028512],
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[0.01677],
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[0.015529],
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[0.01743],
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[0.027563],
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[0.028629],
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[0.019216],
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[0.024677],
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[0.017376],
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[0.014739],
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[0.028112],
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[0.02842]]).squeeze()
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self.test_kurt_0 = (1.904269, .167601)
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self.test_corr = (.012025, .912680,)
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self.test_corr_weights = np.array([[0.020037],
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[0.020108],
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[0.020024],
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[0.02001],
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[0.019766],
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[0.019971],
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[0.020013],
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[0.019663],
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[0.019944],
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[0.01982],
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[0.01983],
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[0.019436],
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[0.020005],
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[0.019897],
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[0.020768],
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[0.020468],
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[0.019521],
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[0.019891],
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[0.020024],
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[0.01997],
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[0.019824],
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[0.019976],
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[0.019979],
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[0.019753],
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[0.020814],
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[0.020474],
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[0.019751],
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[0.020085],
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[0.020087],
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[0.019977],
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[0.020057],
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[0.020435],
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[0.020137],
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[0.020025],
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[0.019982],
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[0.019866],
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[0.020151],
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[0.019046],
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[0.020272],
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[0.020034],
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[0.019813],
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[0.01996],
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[0.020657],
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[0.019947],
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[0.019931],
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[0.02008],
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[0.02035],
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[0.019823],
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[0.02005],
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[0.019497]]).squeeze()
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self.test_joint_skew_kurt = (8.753952, .012563)
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class RegressionResults:
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"""
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Results for EL Regression
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"""
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def __init__(self):
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self.source = 'Matlab'
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self.test_beta0 = (1.758104, .184961, np.array([
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0.04326392, 0.04736749, 0.03573865, 0.04770535, 0.04721684,
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0.04718301, 0.07088816, 0.05631242, 0.04865098, 0.06572099,
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0.04016013, 0.04438627, 0.04042288, 0.03938043, 0.04006474,
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0.04845233, 0.01991985, 0.03623254, 0.03617999, 0.05607242,
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0.0886806]))
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self.test_beta1 = (1.932529, .164482, np.array([
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0.033328, 0.051412, 0.03395, 0.071695, 0.046433, 0.041303,
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0.033329, 0.036413, 0.03246, 0.037776, 0.043872, 0.037507,
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0.04762, 0.04881, 0.05874, 0.049553, 0.048898, 0.04512,
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0.041142, 0.048121, 0.11252]))
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self.test_beta2 = (.494593, .481866, np.array([
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0.046287, 0.048632, 0.048772, 0.034769, 0.048416, 0.052447,
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0.053336, 0.050112, 0.056053, 0.049318, 0.053609, 0.055634,
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0.042188, 0.046519, 0.048415, 0.047897, 0.048673, 0.047695,
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0.047503, 0.047447, 0.026279]))
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self.test_beta3 = (3.537250, .060005, np.array([
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0.036327, 0.070483, 0.048965, 0.087399, 0.041685, 0.036221,
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0.016752, 0.019585, 0.027467, 0.02957, 0.069204, 0.060167,
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0.060189, 0.030007, 0.067371, 0.046862, 0.069814, 0.053041,
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0.053362, 0.041585, 0.033943]))
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self.test_ci_beta0 = (-52.77128837058528, -24.11607348661947)
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self.test_ci_beta1 = (0.41969831751229664, 0.9857167306604057)
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self.test_ci_beta2 = (0.6012045929381431, 2.1847079367275692)
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self.test_ci_beta3 = (-0.3804313225443794, 0.006934528877337928)
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class ANOVAResults:
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"""
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Results for ANOVA
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"""
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def __init__(self):
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self.source = 'Matlab'
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self.compute_ANOVA = (.426163, .51387, np.array([9.582371]), np.array([
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0.018494, 0.01943, 0.016624, 0.0172, 0.016985, 0.01922,
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0.016532, 0.015985, 0.016769, 0.017631, 0.017677, 0.017984,
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0.017049, 0.016721, 0.016382, 0.016566, 0.015642, 0.015894,
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0.016282, 0.015704, 0.016272, 0.015678, 0.015651, 0.015648,
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0.015618, 0.015726, 0.015981, 0.01635, 0.01586, 0.016443,
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0.016126, 0.01683, 0.01348, 0.017391, 0.011225, 0.017282,
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0.015568, 0.017543, 0.017009, 0.016325, 0.012841, 0.017648,
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0.01558, 0.015994, 0.017258, 0.017664, 0.017792, 0.017772,
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0.017527, 0.017797, 0.017856, 0.017849, 0.017749, 0.017827,
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0.017381, 0.017902, 0.016557, 0.015522, 0.017455, 0.017248]))
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class AFTRes:
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"""
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Results for the AFT model from package emplik in R written by Mai Zhou
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"""
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def __init__(self):
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self.test_params = np.array([3.77710799, -0.03281745])
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self.test_beta0 = (.132511, 0.7158323)
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self.test_beta1 = (.297951, .5851693)
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self.test_joint = (11.8068, 0.002730147)
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class OriginResults:
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"""
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These results are from Bruce Hansen's Matlab package.
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To replicate the results, the exogenous variables were scaled
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down by 10**-2 and the results were then rescaled.
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These tests must also test likelihood functions because the
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llr when conducting hypothesis tests is the MLE while
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restricting the intercept to 0. Matlab's generic package always
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uses the unrestricted MLE.
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"""
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def __init__(self):
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self.test_params = np.array([0, .00351861])
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self.test_llf = -1719.793173 # llf when testing param = .0034
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self.test_llf_hat = -1717.95833 # llf when origin=0
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self.test_llf_hypoth = -2*(self.test_llf-self.test_llf_hat)
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self.test_llf_conf = -1719.879077 # The likelihood func at conf_vals
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Reference in New Issue
Block a user