import numpy as np

llf = np.array([ -244.3852892951])

nobs = np.array([             202])

k = np.array([               5])

k_exog = np.array([               1])

sigma = np.array([ .81130812929037])

chi2 = np.array([ 73901.783883385])

df_model = np.array([               3])

k_ar = np.array([               1])

k_ma = np.array([               2])

params = np.array([ .91963917600489, 
                   -.89804855498306, 
                    1.3032353997768, 
                    .30230174935463, 
                    .65822088065264])

cov_params = np.array([ .00622027554245, 
                        .00026933156699, 
                       -.00014478009121, 
                       -.00010527901395, 
                        .00006880952561, 
                        .00026933156699, 
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                       -.00263264462948, 
                       -.00241927046074, 
                       -.00069258998629, 
                       -.00014478009121, 
                       -.00263264462948, 
                          .003637275855, 
                         .0033625395431, 
                        .00072507981262, 
                       -.00010527901395, 
                       -.00241927046074, 
                         .0033625395431, 
                        .00312013997649, 
                        .00067166747844, 
                        .00006880952561, 
                       -.00069258998629, 
                        .00072507981262, 
                        .00067166747844, 
                        .00108477120916]).reshape(5,5)

xb = np.array([ .91963917016983, 
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                 1.127131819725])

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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                 1.611407160759, 
               -.92573761940002, 
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                .63931334018707, 
                .38197138905525, 
               -.27523550391197, 
                 1.404825925827, 
                -.1979645639658, 
                .94665539264679, 
                1.4236577749252, 
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                 -.643210709095, 
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               -.26304465532303, 
                .43178829550743, 
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                .34627133607864, 
               -.19923311471939, 
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                .41377285122871, 
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                .35733091831207, 
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               -.05658006668091, 
               -.09336434304714, 
                .40078815817833, 
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               -.02043732441962, 
                  .217001542449, 
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               -.60332614183426, 
                .34161224961281, 
               -.40931203961372, 
                .11139994114637, 
                .15139718353748, 
               -.09771674126387, 
                .45419663190842, 
                .15193535387516, 
                 .5985592007637, 
                .66923010349274, 
                .02294866926968, 
                .80127727985382, 
               -.46522265672684, 
                .33565619587898, 
               -.94310986995697, 
               -.34323886036873, 
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               -.37864658236504, 
                .37210035324097, 
                 .3631728887558, 
               -.47128081321716, 
               -.20438456535339, 
                 .3103321492672, 
                 .3909215927124, 
                .01124659087509, 
                .81024420261383, 
                .42646199464798, 
               -.01574708521366, 
                 1.044772028923, 
               -.40038701891899, 
                 3.768208026886, 
                -2.273681640625, 
                1.5517644882202, 
                .09178187698126, 
               -1.3350188732147, 
                .91490334272385, 
                 1.317883014679, 
               -.20600511133671, 
                1.1940457820892, 
                1.7408289909363, 
                .15753293037415, 
                3.4885265827179, 
               -3.8985877037048, 
               -3.9952800273895, 
                .87455189228058, 
                .56503582000732, 
                .78886848688126])

mse = np.array([ 1.8363103866577, 
                 .82643759250641, 
                 .69298559427261, 
                 .67553198337555, 
                 .67162227630615, 
                 .67045229673386, 
                 .66999220848083, 
                 .66973727941513, 
                 .66954612731934, 
                 .66937738656998, 
                 .66921871900558, 
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                 .66891890764236, 
                 .66877597570419, 
                 .66863715648651, 
                 .66850233078003, 
                 .66837131977081, 
                 .66824388504028, 
                  .6681199669838, 
                 .66799944639206, 
                  .6678820848465, 
                 .66776782274246, 
                 .66765648126602, 
                 .66754806041718, 
                 .66744232177734, 
                 .66733926534653, 
                 .66723877191544, 
                 .66714072227478, 
                 .66704499721527, 
                 .66695153713226, 
                 .66686034202576, 
                 .66677123308182, 
                 .66668421030045, 
                 .66659909486771, 
                 .66651594638824, 
                 .66643464565277, 
                 .66635513305664, 
                 .66627734899521, 
                 .66620123386383, 
                 .66612672805786, 
                  .6660538315773, 
                 .66598242521286, 
                 .66591250896454, 
                 .66584402322769, 
                 .66577690839767, 
                 .66571110486984, 
                 .66564667224884, 
                 .66558349132538, 
                 .66552156209946, 
                 .66546076536179, 
                 .66540116071701, 
                 .66534268856049, 
                 .66528534889221, 
                  .6652290225029, 
                 .66517376899719, 
                  .6651194691658, 
                 .66506624221802, 
                  .6650139093399, 
                 .66496247053146, 
                 .66491198539734, 
                 .66486239433289, 
                 .66481363773346, 
                  .6647657752037, 
                 .66471868753433, 
                 .66467237472534, 
                 .66462683677673, 
                 .66458207368851, 
                 .66453808546066, 
                 .66449475288391, 
                  .6644521355629, 
                 .66441023349762, 
                 .66436898708344, 
                 .66432839632034, 
                 .66428846120834, 
                 .66424912214279, 
                 .66421037912369, 
                 .66417229175568, 
                 .66413474082947, 
                 .66409778594971, 
                 .66406136751175, 
                 .66402548551559, 
                 .66399013996124, 
                 .66395533084869, 
                  .6639209985733, 
                 .66388720273972, 
                 .66385388374329, 
                 .66382104158401, 
                 .66378861665726, 
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                 .66372525691986, 
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                 .66357427835464, 
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                 .66351670026779, 
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                 .66301596164703, 
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                 .66292977333069, 
                 .66290885210037, 
                 .66288816928864, 
                 .66286766529083, 
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                 .66282749176025, 
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                 .66276878118515, 
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                 .66273081302643, 
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                  .6626752614975, 
                 .66265720129013, 
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                 .66260403394699, 
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                 .66250270605087, 
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                 .66245430707932, 
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                 .66216617822647, 
                 .66215318441391, 
                 .66214030981064, 
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                 .66204130649567, 
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                 .66200596094131, 
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                 .66196030378342, 
                 .66194915771484, 
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                 .66192710399628, 
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                 .66189467906952, 
                 .66188406944275, 
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                 .66180229187012, 
                 .66179245710373, 
                 .66178268194199, 
                 .66177302598953, 
                 .66176337003708])

stdp = np.array([ .91963917016983, 
                  .91963917016983, 
                  .69261693954468, 
                  .76115423440933, 
                     .63710719347, 
                  .77478265762329, 
                  .61527073383331, 
                  .80501782894135, 
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                  .72158020734787, 
                  .66619211435318, 
                  .72980058193207, 
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                   .6069877743721, 
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                  .68147450685501, 
                  .72765469551086, 
                  .69699174165726, 
                  .69102382659912, 
                  .67753201723099, 
                  .72617518901825, 
                  .69977235794067, 
                    .713603079319, 
                  .72478419542313, 
                  .68503832817078, 
                  .75677126646042, 
                  .79034942388535, 
                  .68609654903412, 
                  .83208250999451, 
                  .60605573654175, 
                  .80849212408066, 
                  .69862711429596, 
                  .80978256464005, 
                   .7383074760437, 
                  .78789436817169, 
                  .79390448331833, 
                  .79169547557831, 
                  .76283228397369, 
                  .87939429283142, 
                  .75783687829971, 
                  .85010063648224, 
                  .80657452344894, 
                  .86508285999298, 
                  .72368890047073, 
                  .86846202611923, 
                  .75351697206497, 
                  .74047154188156, 
                  .82022970914841, 
                  .73184186220169, 
                   .7623735666275, 
                  .74929028749466, 
                  .75702118873596, 
                  .79036456346512, 
                  .81429827213287, 
                  1.0007030963898, 
                  .70464313030243, 
                  1.2375881671906, 
                  .82733017206192, 
                  1.1780800819397, 
                  .83767229318619, 
                  1.3407131433487, 
                   .7835128903389, 
                  .99667322635651, 
                  .82677388191223, 
                  1.0330017805099, 
                  .78713357448578, 
                  .80603551864624, 
                  .91298097372055, 
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                  .83088356256485, 
                  1.1405943632126, 
                  .72683191299438, 
                  1.0197489261627, 
                  .88344657421112, 
                  1.1016070842743, 
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                  1.1717364788055, 
                  .94894939661026, 
                   1.418029665947, 
                  1.2063212394714, 
                  1.3504880666733, 
                  1.4053744077682, 
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                  .91192328929901, 
                  1.4546687602997, 
                  1.2100585699081, 
                  1.2459771633148, 
                  1.2914154529572, 
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                  .80550068616867, 
                  .88859277963638, 
                  1.5257360935211, 
                  .49089628458023, 
                  .75268715620041, 
                  .92090040445328, 
                  .99410575628281, 
                  .87882828712463, 
                  1.1253950595856, 
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                   .9317963719368, 
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                  .82737028598785, 
                  1.0978132486343, 
                  .74325948953629, 
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                  .03625157848001, 
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                  .51377469301224, 
                  1.3643686771393, 
                  .70055514574051, 
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                  1.1997950077057, 
                  .75360465049744, 
                  1.2862613201141, 
                  .79965251684189, 
                  1.2606881856918, 
                   1.018030166626, 
                  1.1752370595932, 
                  .69517260789871, 
                   1.597958445549, 
                  .65335071086884, 
                  1.4763361215591, 
                  1.2708671092987, 
                  1.0432199239731, 
                    .561203956604, 
                  1.2630445957184, 
                  .66821777820587, 
                  1.2384748458862, 
                  .70777904987335, 
                  1.2246036529541, 
                  .75373476743698, 
                   1.199233174324, 
                  .69312900304794, 
                  1.0659650564194, 
                  .80386221408844, 
                  .99243313074112, 
                  .78622406721115, 
                  1.1766475439072, 
                  .74267518520355, 
                  1.1679803133011, 
                  .85658311843872, 
                  .99335825443268, 
                  .79920876026154, 
                  1.1595865488052, 
                  .92043119668961, 
                  .98299539089203, 
                  .94316083192825, 
                  1.0661553144455, 
                   .6393609046936, 
                   .9456650018692, 
                  .91597771644592, 
                  .80332309007645, 
                  .65838772058487, 
                  1.1093089580536, 
                  .68860310316086, 
                  1.0485997200012, 
                  .89771980047226, 
                  .94581252336502, 
                  1.0480616092682, 
                  1.0014315843582, 
                  1.1307729482651, 
                  1.1770483255386, 
                  .89873492717743, 
                  1.2652103900909, 
                  .66434383392334, 
                  1.1431220769882, 
                  .44322970509529, 
                   .9269899725914, 
                  1.0786435604095, 
                  .82789659500122, 
                  1.0368362665176, 
                  1.0712716579437, 
                  .70438456535339, 
                  .88966482877731, 
                   1.009087562561, 
                  1.0887442827225, 
                  .88976800441742, 
                  1.2735350131989, 
                  1.0157470703125, 
                  .95522791147232, 
                  1.3003809452057, 
                  .73179203271866, 
                  2.4736785888672, 
                 -.25176140666008, 
                  1.9082181453705, 
                  .53501582145691, 
                   .7591078877449, 
                  1.0281100273132, 
                  1.6240043640137, 
                  .60095232725143, 
                  1.6211705207825, 
                   1.344465970993, 
                   1.124480009079, 
                  2.1775946617126, 
                 -.71973150968552, 
                 -.37754261493683, 
                  1.2329530715942, 
                   1.127131819725])

icstats = np.array([             202, 
                    np.nan, 
                     -244.3852892951, 
                                   5, 
                      498.7705785902, 
                     515.31191707721])

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, )