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ex_arch_canada.py
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ex_arch_canada.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Dec 24 07:31:47 2011
Author: Josef Perktold
"""
import numpy as np
import statsmodels.sandbox.stats.diagnostic as dia
canada_raw = '''\
405.36646642737 929.610513893698 7.52999999999884 386.136109062605
404.639833965913 929.803984550587 7.69999999999709 388.135759111711
403.814883043744 930.318387567177 7.47000000000116 390.540112911955
404.215773188006 931.427687420772 7.2699999999968 393.963817246136
405.046713585284 932.662005594273 7.37000000000262 396.764690917547
404.416738673847 933.550939726636 7.12999999999738 400.021701616327
402.81912737043 933.531526191785 7.40000000000146 400.751498688807
401.977334663103 933.076879439814 8.33000000000175 405.733473658807
402.089724946428 932.12375320915 8.83000000000175 409.05038628366
401.306688373207 930.635939140315 10.429999999993 411.398377747425
401.630171263522 929.097059933419 12.1999999999971 413.019421511595
401.56375463175 928.563335601161 12.7700000000041 415.166962884156
402.815698906973 929.069380060201 12.429999999993 414.662070678749
403.142107624713 930.265516098198 12.2299999999959 415.731936138368
403.078619166324 931.677031559203 11.6999999999971 416.231468866173
403.718785733801 932.138967575148 11.1999999999971 418.14392690728
404.866799027579 932.276686471608 11.2700000000041 419.735231229658
405.636186735378 932.832783118083 11.4700000000012 420.484186198549
405.136285378794 933.733419116009 11.3000000000029 420.930881402259
406.024639922986 934.177206176622 11.1699999999983 422.112404525291
406.412269729241 934.592839827856 11 423.627805811063
406.300932644569 935.606709830033 10.6300000000047 423.988686751336
406.335351723382 936.511085968336 10.2700000000041 424.190212657915
406.773695329549 937.420090112655 10.1999999999971 426.127043353785
405.152547649247 938.415921627889 9.66999999999825 426.857794216679
404.929830809648 938.999170021426 9.60000000000582 426.745717993024
404.576546350926 939.235354789206 9.60000000000582 426.885793656802
404.199492630983 939.679504234357 9.5 428.840253264144
405.94985619596 940.249674139969 9.5 430.122322107039
405.82209202516 941.435818685214 9.02999999999884 430.230679154048
406.446282537108 942.29809597644 8.69999999999709 430.392994893689
407.051247525876 943.532223256403 8.13000000000466 432.028420083791
407.946023990985 944.34896981513 7.87000000000262 433.388625934544
408.179584663105 944.821488789039 7.66999999999825 433.964091817787
408.599812740441 945.067136927327 7.80000000000291 434.484384354647
409.090560656008 945.80672616174 7.7300000000032 436.156879277168
408.704215141145 946.869661504613 7.56999999999971 438.265143944308
408.980275213206 946.876612143542 7.56999999999971 438.763587343863
408.328690037174 947.249692256472 7.33000000000175 439.949811558539
407.885696563307 947.651276093962 7.56999999999971 441.835856392131
407.260532233258 948.183970741596 7.62999999999738 443.176872656863
406.775150765526 948.349239264364 7.59999999999854 444.359199033223
406.179413590339 948.032170661406 8.16999999999825 444.523614807208
405.439793348166 947.106483115935 9.19999999999709 446.969404642587
403.279970790458 946.079554231134 10.1699999999983 450.158586973168
403.364855995771 946.183811678692 10.3300000000017 451.546427290378
403.380680430043 946.22579516585 10.3999999999942 452.298351499968
404.003182812546 945.997783938785 10.3699999999953 453.120066578834
404.47739841708 945.518279080208 10.6000000000058 453.999145996277
404.786782762866 945.351397570438 11 454.955176222477
405.271003921828 945.291785517556 11.3999999999942 455.482381155116
405.382993140508 945.400785900878 11.7299999999959 456.100929020225
405.156416006566 945.905809840959 11.070000000007 457.202696739531
406.470043094757 945.90347041344 11.6699999999983 457.388589594786
406.229308967752 946.319028746014 11.4700000000012 457.779898919191
406.726483850871 946.579621275764 11.3000000000029 457.553538085846
408.578504884277 946.780032223884 10.9700000000012 458.80240271533
409.67671010704 947.628284240641 10.6300000000047 459.05640335985
410.385763295936 948.622057553611 10.1000000000058 459.15782324686
410.539523677181 949.399183241404 9.66999999999825 459.703720275789
410.445258303139 949.948137966398 9.52999999999884 459.703720275789
410.625605270832 949.794494142446 9.47000000000116 460.025814162716
410.867239714014 949.953380175189 9.5 461.025722503696
411.235917829196 950.250239444989 9.27000000000407 461.30391443673
410.663655285725 950.538030883093 9.5 461.4030814421
410.808508412624 950.787128498243 9.42999999999302 462.927726133156
412.115961520089 950.869528648471 9.69999999999709 464.688777934061
412.999407129539 950.928132469716 9.89999999999418 465.071700094375
412.955056755303 951.845722481401 9.42999999999302 464.285125295526
412.82413309368 952.6004761952 9.30000000000291 464.034426099541
413.048874899 953.597552755418 8.86999999999534 463.453479461824
413.611017876145 954.143388344158 8.77000000000407 465.071700094375
413.604781916778 954.542593332134 8.60000000000582 466.088867474481
412.968388225217 955.263136106029 8.33000000000175 466.617120754625
412.265886525002 956.056052852469 8.16999999999825 465.747796561181
412.910594097915 956.79658640007 8.02999999999884 465.899527268299
413.829416419695 957.386480451857 7.90000000000146 466.409925351738
414.22415210314 958.06341570725 7.87000000000262 466.955244491812
415.1677707968 958.716592187518 7.52999999999884 467.628081344681
415.701580225863 959.488142422254 6.93000000000029 467.70256230891
416.867407108435 960.362493080892 6.80000000000291 469.134788222928
417.610399060359 960.783379042937 6.69999999999709 469.336419672322
418.002980476361 961.029029939624 6.93000000000029 470.011666329664
417.266680178544 961.765709811429 6.87000000000262 469.647234439539'''
canada = np.array(canada_raw.split(), float).reshape(-1,4)
k=2;
resarch2 = dia.acorr_lm((canada[:,k]-canada[:,k].mean())**2, maxlag=2, autolag=None, store=1)
print resarch2
resarch5 = dia.acorr_lm(canada[:,k]**2, maxlag=12, autolag=None, store=1)
ss = '''\
ARCH LM-test; Null hypothesis: no ARCH effects
Chi-squared = %(chi)-8.4f df = %(df)-4d p-value = %(pval)8.4g
'''
resarch = resarch5
print
print ss % dict(chi=resarch[2], df=resarch[-1].resols.df_model, pval=resarch[3])
#R:FinTS: ArchTest(as.vector(Canada[,3]), lag=5)
'''
ARCH LM-test; Null hypothesis: no ARCH effects
data: as.vector(Canada[, 3])
Chi-squared = 78.878, df = 5, p-value = 1.443e-15
'''
#from ss above
'''
ARCH LM-test; Null hypothesis: no ARCH effects
Chi-squared = 78.849 df = 5 p-value = 1.461e-15
'''
#k=2
#R
'''
ARCH LM-test; Null hypothesis: no ARCH effects
data: as.vector(Canada[, 4])
Chi-squared = 74.6028, df = 5, p-value = 1.121e-14
'''
#mine
'''
ARCH LM-test; Null hypothesis: no ARCH effects
Chi-squared = 74.6028 df = 5 p-value = 1.126e-14
'''
'''
> ArchTest(as.vector(Canada[,4]), lag=12)
ARCH LM-test; Null hypothesis: no ARCH effects
data: as.vector(Canada[, 4])
Chi-squared = 69.6359, df = 12, p-value = 3.747e-10
'''
#mine:
'''
ARCH LM-test; Null hypothesis: no ARCH effects
Chi-squared = 69.6359 df = 12 p-value = 3.747e-10
'''