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st: A cry for help on ARMA with weak autocorrelation
From
"Dmytro Andriychenko" <[email protected]>
To
<[email protected]>
Subject
st: A cry for help on ARMA with weak autocorrelation
Date
Wed, 5 May 2010 19:49:37 +0100
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 | |
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