import numpy as np

llf = np.array([-242.06033399744])

nobs = np.array([             202])

k = np.array([               4])

k_exog = np.array([               1])

sigma = np.array([ .80201496146073])

chi2 = np.array([ 348.43324197088])

df_model = np.array([               2])

k_ar = np.array([               1])

k_ma = np.array([               1])

params = np.array([ .82960638524364, 
                    .93479332833705, 
                   -.75728342544279, 
                    .64322799840686])

cov_params = np.array([ .14317811930738, 
                       -.01646077810033, 
                        .01510986837498, 
                       -.00280799533479, 
                       -.01646077810033, 
                        .00321032468661, 
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                        .00097645385252, 
                        .01510986837498, 
                       -.00353027620719, 
                        .00484312817753, 
                       -.00112050648944, 
                       -.00280799533479, 
                        .00097645385252, 
                       -.00112050648944, 
                         .0007715609499]).reshape(4,4)

xb = np.array([ .82960641384125, 
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                .31728908419609, 
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                .60612773895264])

y = np.array([np.nan, 
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resid = np.array([np.nan, 
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yr = np.array([np.nan, 
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               -.23930950462818, 
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                .51469951868057, 
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                .06622361391783, 
                .54170626401901, 
                   .53436845541, 
                    .2353515625, 
                .29585054516792, 
                .04819770529866, 
                .24760706722736, 
                .31166675686836, 
                .36669155955315, 
                .21487690508366, 
                .79340130090714, 
                .17062658071518, 
               -.33359375596046, 
                .95196217298508, 
                .10373862832785, 
                .31576481461525, 
                 1.589346408844, 
               -.26140204071999, 
               -1.0672763586044, 
               -.13626158237457, 
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                .01412893645465, 
                 .1283223181963, 
                .02133745700121, 
               -.06621573865414, 
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                .13481116294861, 
               -.28028702735901, 
               -.02071117423475, 
                .28890857100487, 
                .04294065013528, 
                .14363515377045, 
                .32640132308006, 
               -.22214868664742, 
                -.0701690018177, 
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               -.14886146783829, 
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                .16944620013237, 
               -.54752624034882, 
               -.43612506985664, 
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               -.62015581130981, 
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               -.06979911774397, 
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                 .4273681640625, 
               -.05220314115286, 
                .55854320526123, 
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                .72063958644867, 
                .00899865385145, 
                .53098171949387, 
               -.44116449356079, 
               -.13600566983223, 
               -.89187180995941, 
               -.81647485494614, 
                .83413660526276, 
               -.21809615194798, 
                .32638800144196, 
                .47133237123489, 
                -.4058920443058, 
               -.42233863472939, 
                .35867437720299, 
                .49578228592873, 
                .11262346804142, 
                .70294010639191, 
                .58906590938568, 
               -.19715182483196, 
                .86181098222733, 
               -.37105345726013, 
                3.3236031532288, 
                -1.543759226799, 
               -.11011194437742, 
                .64728397130966, 
               -2.2335081100464, 
                .67635416984558, 
                1.2392344474792, 
                .10933646559715, 
                .49816474318504, 
                2.0072033405304, 
               -.17484994232655, 
                3.0224411487579, 
               -3.7984521389008, 
               -6.0368394851685, 
                .27887633442879, 
                1.4904805421829, 
                1.3098726272583])

mse = np.array([ 1.0121052265167, 
                 .66349595785141, 
                 .65449619293213, 
                 .64957880973816, 
                 .64683443307877, 
                 .64528465270996, 
                 .64440369606018, 
                 .64390099048615, 
                 .64361357688904, 
                 .64344894886017, 
                 .64335465431213, 
                 .64330065250397, 
                 .64326965808868, 
                 .64325189590454, 
                 .64324170351028, 
                  .6432358622551, 
                 .64323252439499, 
                 .64323055744171, 
                 .64322948455811, 
                 .64322882890701, 
                 .64322847127914, 
                 .64322829246521, 
                 .64322817325592, 
                 .64322811365128, 
                 .64322805404663, 
                 .64322805404663, 
                 .64322799444199, 
                 .64322799444199, 
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stdp = np.array([ .82960641384125, 
                  .82960641384125, 
                    .697261095047, 
                  .61113905906677, 
                  .51607495546341, 
                  .47362637519836, 
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                  .30664679408073, 
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                  .28816330432892, 
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                  .30216124653816, 
                  .30066826939583, 
                  .31728908419609, 
                  .30679926276207, 
                   .3272570669651, 
                  .37292611598969, 
                  .36668366193771, 
                  .40278288722038, 
                  .36799272894859, 
                  .36827209591866, 
                  .38623574376106, 
                  .39983862638474, 
                  .42789059877396, 
                  .43138384819031, 
                  .46953064203262, 
                  .48066720366478, 
                  .48910140991211, 
                  .53098994493484, 
                  .54496067762375, 
                  .55554050207138, 
                  .58130383491516, 
                  .60081332921982, 
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                  .58214038610458, 
                  .58369606733322, 
                  .53162068128586, 
                  .54543834924698, 
                  .52040082216263, 
                  .50143963098526, 
                  .48708060383797, 
                  .47620677947998, 
                  .48572361469269, 
                  .51068127155304, 
                  .61833620071411, 
                  .61110657453537, 
                  .76539021730423, 
                  .84672522544861, 
                  .92606955766678, 
                  .96840506792068, 
                  1.0892199277878, 
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                  .97345739603043, 
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                  .87280809879303, 
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                  .95647835731506, 
                  1.0624366998672, 
                  1.1426770687103, 
                  1.1679404973984, 
                   1.311328291893, 
                   1.473167181015, 
                  1.5602221488953, 
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                  1.8328944444656, 
                  1.7793855667114, 
                  1.5791050195694, 
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                  1.5250737667084, 
                  1.3155146837234, 
                   1.014811873436, 
                  .98235523700714, 
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                  .97035628557205, 
                  1.0196926593781, 
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                  .98315137624741, 
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                  1.0333099365234, 
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                  1.1066001653671, 
                  1.2293750047684, 
                   1.233595252037, 
                  1.1480363607407, 
                  1.2962552309036, 
                  1.2842413187027, 
                  1.3106474876404, 
                  1.5614050626755, 
                  1.4672855138779, 
                  1.2362524271011, 
                  1.1855486631393, 
                  1.1294020414352, 
                  1.1046353578568, 
                  1.0858771800995, 
                  1.0716745853424, 
                  1.0786685943604, 
                  1.0662157535553, 
                  1.0390332937241, 
                  .96519494056702, 
                   .9802839756012, 
                  .92070508003235, 
                  .91108840703964, 
                  .95705932378769, 
                  .95637094974518, 
                  .97360169887543, 
                  1.0221517086029, 
                   .9701629281044, 
                  .94854199886322, 
                  .98542231321335, 
                   1.048855304718, 
                  1.0081344842911, 
                  1.0305507183075, 
                  1.0475262403488, 
                  .93612504005432, 
                  .85176283121109, 
                  .89438372850418, 
                    .820152759552, 
                  .71068543195724, 
                  .76979607343674, 
                  .76130604743958, 
                  .77262878417969, 
                  .85220617055893, 
                  .84146595001221, 
                  .93983960151672, 
                  .97883212566376, 
                  1.0793634653091, 
                  1.1909983158112, 
                  1.1690304279327, 
                  1.2411522865295, 
                  1.1360056400299, 
                  1.0918840169907, 
                   .9164656996727, 
                  .76586949825287, 
                    .918093085289, 
                  .87360894680023, 
                  .92867678403854, 
                    1.00588285923, 
                  .92233866453171, 
                  .84132260084152, 
                  .90422683954239, 
                   .9873673915863, 
                  .99707210063934, 
                  1.1109310388565, 
                  1.1971517801285, 
                   1.138188958168, 
                  1.2710473537445, 
                  1.1763968467712, 
                  1.7437561750412, 
                  1.4101150035858, 
                  1.3527159690857, 
                  1.4335050582886, 
                  .99765706062317, 
                  1.1067585945129, 
                  1.3086627721786, 
                  1.2968333959579, 
                  1.3547962903976, 
                  1.6768488883972, 
                  1.5905654430389, 
                  2.0774590969086, 
                  1.3218278884888, 
                  .21813294291496, 
                  .30750840902328, 
                  .60612773895264])

icstats = np.array([             202, 
                    np.nan, 
                    -242.06033399744, 
                                   4, 
                     492.12066799488, 
                     505.35373878448])

class Bunch(dict):
    def __init__(self, **kw):
        dict.__init__(self, kw)
        self.__dict__  = self


results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, )