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Re: st: A cry for help on ARMA with weak autocorrelation
From
Tirthankar Chakravarty <[email protected]>
To
[email protected]
Subject
Re: st: A cry for help on ARMA with weak autocorrelation
Date
Thu, 6 May 2010 06:04:40 +0530
This is symptomatic of GARCH effects:
****************************************
clear*
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end
g id = _n
tsset id
tsline dmytro
reg dmytro
estat archlm, lags(1(1)10)
arch dmytro, arch(1) garch(1)
****************************************
T
2010/5/6 Robert A Yaffee <[email protected]>:
> Dmytro,
> It sounds as if you have more noise than signal. If you cannot
> decompose that into any signal, you may merely have white noise.
> - Robert
>
>
> Robert A. Yaffee, Ph.D.
> Research Professor
> Silver School of Social Work
> New York University
>
> Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf
>
> CV: http://homepages.nyu.edu/~ray1/vita.pdf
>
> ----- Original Message -----
> From: Dmytro Andriychenko <[email protected]>
> Date: Wednesday, May 5, 2010 2:50 pm
> Subject: st: A cry for help on ARMA with weak autocorrelation
> To: [email protected]
>
>
>> Dear Statalist,
>>
>> I have been recently asked to fit a univariate model for a particular
>> stationary time series. I thought ARMA will be the obvious choice, but
>> when
>> I looked at the autocorrelation within the series, I found that the first
>> two lags are not significant and the few significant ones are only
>> marginally so (see corrgram output below).
>>
>> I guess I can still do the estimates, but I am very much wondering if
>> ARMA
>> is appropriate modeling technique here. Is it even valid? Portmanteau
>> statistics is not rejecting hypothesis of no autocorrelation and Q stats
>> suggest marginal significance at lag 3,5 and 7 in the first series.
>>
>> The question is: is it even appropriate to be building ARMA in the
>> light of
>> such weak autocorrelation, especially that the first two lags are not
>> significant? If ARMA is not appropriate, then what can it be?
>>
>> If anyone can help me with that, or better still point me towards a
>> reference that would explain that, I would very much extremely appreciate
>> that. I have been reading books on econometrics for over a week, but still
>> cannot conclusively answer the question.
>>
>> Thank you,
>>
>> Dmytro
>>
>> -1 0 1 -1
>> 0
>> 1
>> LAG AC PAC Q Prob>Q [Autocorrelation] [Partial
>> Autocor]
>> ----------------------------------------------------------------------------
>> ---
>> 1 -0.0516 -0.0516 1.3555 0.2443 |
>> |
>>
>> 2 0.0574 0.0551 3.0348 0.2193 |
>> |
>>
>> 3 -0.1253 -0.1209 11.062 0.0114 -|
>> |
>>
>> 4 -0.0248 -0.0401 11.377 0.0226 |
>> |
>>
>> 5 0.0887 0.1020 15.417 0.0087 |
>> |
>>
>> 6 0.0809 0.0808 18.785 0.0045 |
>> |
>>
>> 7 -0.1274 -0.1468 27.146 0.0003 -| -|
>>
>> 8 0.0455 0.0491 28.214 0.0004 |
>> |
>>
>> 9 -0.0718 -0.0235 30.882 0.0003 |
>> |
>>
>> 10 -0.0196 -0.0722 31.081 0.0006 |
>> |
>>
>> 11 0.0029 -0.0091 31.085 0.0011 |
>> |
>>
>> 12 -0.0930 -0.0818 35.588 0.0004 |
>> |
>>
>> 13 0.1613 0.1684 49.154 0.0000 |-
>> |-
>>
>> 14 -0.0064 -0.0050 49.176 0.0000 |
>> |
>>
>> 15 0.0465 0.0277 50.305 0.0000 |
>> |
>>
>> 16 0.0520 0.0951 51.726 0.0000 |
>> |
>>
>> 17 -0.0261 -0.0090 52.084 0.0000 |
>> |
>>
>> 18 0.0407 0.0249 52.955 0.0000 |
>> |
>>
>> 19 0.0720 0.0485 55.692 0.0000 |
>> |
>>
>> 20 -0.0093 0.0368 55.738 0.0000 |
>> |
>>
>> 21 0.0882 0.0593 59.865 0.0000 |
>> |
>>
>> 22 0.0209 0.0623 60.098 0.0000 |
>> |
>>
>> 23 -0.0231 -0.0031 60.381 0.0000 |
>> |
>>
>> 24 -0.0112 -0.0256 60.448 0.0001 |
>> |
>>
>> 25 -0.0611 -0.0094 62.445 0.0000 |
>> |
>>
>> 26 0.0820 0.0641 66.046 0.0000 |
>> |
>>
>> 27 -0.0818 -0.1022 69.64 0.0000 |
>> |
>>
>> 28 -0.0372 -0.0531 70.384 0.0000 |
>> |
>>
>> 29 -0.0148 0.0117 70.503 0.0000 |
>> |
>>
>> 30 -0.0195 -0.0182 70.708 0.0000 |
>> |
>>
>> 31 0.0606 0.0384 72.693 0.0000 |
>> |
>>
>> 32 0.0101 -0.0140 72.748 0.0001 |
>> |
>>
>> 33 0.0248 0.0807 73.083 0.0001 |
>> |
>>
>> 34 0.0049 -0.0336 73.096 0.0001 |
>> |
>>
>> 35 -0.0110 -0.0529 73.162 0.0002 |
>> |
>>
>> 36 -0.0420 -0.0467 74.129 0.0002 |
>> |
>>
>> 37 0.0684 0.0485 76.693 0.0001 |
>> |
>>
>> 38 0.0291 0.0632 77.159 0.0002 |
>> |
>>
>> 39 0.0517 -0.0230 78.631 0.0002 |
>> |
>>
>> 40 -0.0336 0.0225 79.255 0.0002 |
>> |
>>
>>
>>
>> The values are:
>>
>> -.0090714
>> .0218658
>> -.0268755
>> .0024567
>> -.056356
>> .0046611
>> .0136881
>> -.0091224
>> .0054574
>> -.0334902
>> .0212493
>> .0597358
>> -.0207405
>> .0116024
>> -.01581
>> -.0096569
>> .0243721
>> -.0002346
>> -.0107546
>> -.0070105
>> .0029306
>> .0054045
>> .0222607
>> .0032482
>> .033515
>> -.0261011
>> -.0244341
>> -.0354443
>> .0121222
>> .0120258
>> -.0312228
>> .0112433
>> .0132771
>> .0068617
>> .0016813
>> .0044956
>> -.0182991
>> -.0232587
>> .0172067
>> -.0174499
>> -.0107841
>> .019073
>> -.0025773
>> -.0036931
>> .0090313
>> .0116596
>> -.0042143
>> -.01231
>> -.0116882
>> -.0226994
>> -.0131874
>> .007977
>> -.05966
>> -.0327191
>> .0383449
>> -.0062823
>> .0268879
>> .0207028
>> .0112748
>> -.0086665
>> .0050945
>> .0044184
>> -.0026326
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>> -.0119662
>> .026412
>> .0141501
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>> -.016212
>> .0151157
>> -.0394745
>> .0161915
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>> .0185943
>> -.0217462
>> -.0145679
>> .0094967
>> -.0019608
>> -.0120802
>> .0098996
>> -.0255461
>> .0237536
>> .0244961
>> .0004768
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>> .0042987
>> -.0058708
>> -.0125189
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>> -.015285
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>> -.0084252
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>> -.0313945
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>> .0077167
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>> .0250711
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>> -.0089717
>> .0117569
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>> .0135822
>> -.0033212
>> .0096278
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>> -.0016627
>> -.0127578
>> .0227208
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>> .006104
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>> .0258164
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>> .0090237
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>> .0119371
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>> .0068007
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>> .0127311
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>> -.0135465
>> .0038967
>> .0044174
>> .0219884
>> -.0048823
>> .0122457
>> -.0176673
>> -.009655
>> -.0123987
>> .0232635
>> .004787
>> .0067487
>> .0085082
>> -.0185409
>> -.0008988
>> -.0144238
>> -.002049
>> .0013633
>> .0054221
>> .0073538
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>> -.0047631
>> .0170636
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>> -.0078974
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>> -.0015335
>> -.0459242
>> .0222826
>> -.0173922
>> .0336294
>> -.004158
>> .0146761
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>> -.0069933
>> .0115929
>> -.0105085
>> .0153751
>> -.0298243
>> .0334148
>> -.025835
>> .0040855
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>> -.0174904
>> -.0595789
>> .0040874
>> -.0296612
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>> .0108528
>> .0232458
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>> -.0044308
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>> -.0052414
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>> -.0313873
>> -.018117
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>> -.0278697
>> .0277452
>> -.0048842
>> .0003047
>> .0267324
>> -.0051508
>> .0073652
>> .0230937
>> -.0676994
>> .027998
>> -.0175819
>> -.0480194
>> -.0234528
>> -.2254887
>> .0186381
>> -.050343
>> .1131096
>> .0031643
>> -.0352201
>> -.1120925
>> .1211257
>> -.0579996
>> .0575261
>> .0037999
>> -.0139828
>> .0747881
>> -.0212183
>> -.0686975
>> -.0238781
>> .0235171
>> .0349255
>> -.021409
>> -.0750003
>> -.0129738
>> -.0756612
>> .0597296
>> .0227227
>> .0146079
>> .0395327
>> -.0081658
>> .0301604
>> .0145698
>> .0219765
>> .0479522
>> -.0284438
>> .0047235
>> .0110078
>> .0070996
>> 9.06e-06
>> -.0253553
>> -.0203786
>> -.0011497
>> -.0259624
>> .059896
>> .0423455
>> .0072837
>> .0299325
>> -.0012712
>> .0270276
>> .0129409
>> -.0113106
>> .0352964
>> .0290713
>> -.0188484
>> -.0181832
>> .0361772
>> .0055947
>> .0070224
>> -.0397735
>> .0190506
>> .029408
>> -.0102329
>>
>>
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
--
To every ω-consistent recursive class κ of formulae there correspond
recursive class signs r, such that neither v Gen r nor Neg(v Gen r)
belongs to Flg(κ) (where v is the free variable of r).
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/