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Megh Dal

2007-Aug-16 10:57 UTC

[R] ADF test

Hi all,
   
  Hope you people do not feel irritated for repeatedly sending mail on Time
series.
   
  Here I got another problem on the same, and hope I would get some answer from
you.
   
  I have following dataset:
   
  data[,1]
  [1] 4.96 4.95 4.96 4.96 4.97 4.97 4.97 4.97 4.97 4.98 4.98 4.98 4.98 4.98 4.99
4.99 5.00 5.01
 [19] 5.01 5.00 5.01 5.01 5.01 5.01 5.02 5.01 5.02 5.02 5.03 5.03 5.03 5.03 5.03
5.04 5.04 5.04
 [37] 5.04 5.04 5.04 5.05 5.05 5.06 5.06 5.06 5.07 5.07 5.07 5.07 5.08 5.07 5.08
5.08 5.09 5.10
 [55] 5.10 5.09 5.10 5.10 5.10 5.10 5.10 5.10 5.10 5.10 5.11 5.11 5.11 5.11 5.11
5.11 5.11 5.12
 [73] 5.12 5.12 5.12 5.13 5.14 5.14 5.14 5.14 5.14 5.15 5.15 5.15 5.15 5.14 5.15
5.15 5.15 5.16
 [91] 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.17 5.17 5.17 5.17 5.17
5.18 5.18 5.18
[109] 5.18 5.18 5.19 5.19 5.20 5.20 5.20 5.20 5.20 5.21 5.21 5.21 5.21 5.21 5.21
5.22 5.22 5.23
[127] 5.23 5.23 5.23 5.24 5.24 5.24 5.25 5.24 5.24 5.25 5.26 5.26 5.26 5.26 5.26
5.26 5.26 5.27
[145] 5.27 5.26 5.27 5.27 5.28 5.29 5.29 5.29 5.29 5.30 5.30 5.30 5.31 5.31 5.31
5.32 5.32 5.33
[163] 5.33

   
  Now I want to conduct a test for stationarity using ADF test :
   
  > adf.test((data[,1]), "stationary",  0)
          Augmented Dickey-Fuller Test
  data:  (data[, 1]) 
Dickey-Fuller = -3.7351, Lag order = 0, p-value = 0.02394
alternative hypothesis: stationary 

  But surprisingly it leads towards rejestion of NULL [p-value is less than
0.05], i.e. indicates a possible stationary series. However ploting a graph of
actual data set it doesn't seem so.
   
  Am I making any mistakes ? Can anyone give me any suggestion?
   
  Regards,
  Megh

       
---------------------------------

	[[alternative HTML version deleted]]

gyadav at ccilindia.co.in

2007-Aug-16 11:29 UTC

head link

[R] ADF test

Hi Megh

i hope you have confused with 'what is my NULL hypothesis' ?
i suggest you to take any ideal dataset about which you know that whether 
it is stationary or not ? apply the test to know what is the NULL 
hypothesis 
used in any software :)
usually in many softwares the NULL hypothesis is in negative sense. Please 
everybody comment on this :)

hoping that you series is return series and not price series :). Thus 
applying adf test for your series :)
my test show that your series is not stationary at all as my correlalogram 
comes as follows. 
1
0.998283718
0.997582959
0.99703921
0.99665648
0.996548006
0.99647617
0.995925698
0.995317271
0.994746317
0.994727781
0.99508777
0.99501576
0.99437404
0.993338292
0.992684933
0.992310313

@@@ HHHHmmmmmmmmm
Although if i assume that your series is a price series and defining 
return = 100*ln(pt/pt-1). Returns become as follows
0
-0.201816416
0.201816416
0
0.201409937
0
0
0
0
0.201005093
0
0
0
0
0.200601873
0
0.200200267
0.199800266
0
-0.199800266
0.199800266
0
0
0
0.199401861
-0.199401861
0.199401861
0
0.199005041
0
0
0
0
0.198609797
0
0
0
0
0
0.19821612
0
0.197824001
0
0
0.19743343
0
0
0
0.197044399
-0.197044399
0.197044399
0
0.196656897
0.196270917
0
-0.196270917
0.196270917
0
0
0
0
0
0
0
0.195886449
0
0
0
0
0
0
0.195503484
0
0
0
0.195122013
0.194742028
0
0
0
0
0.194363521
0
0
0
-0.194363521
0.194363521
0
0
0.193986482
0
0
0
0
0
0
0
0
0
0
0.193610903
0
0
0
0
0.193236775
0
0
0
0
0.192864091
0
0.192492841
0
0
0
0
0.192123018
0
0
0
0
0
0.191754613
0
0.191387618
0
0
0
0.191022026
0
0
0.190657827
-0.190657827
0
0.190657827
0.190295015
0
0
0
0
0
0
0.18993358
0
-0.18993358
0.18993358
0
0.189573516
0.189214815
0
0
0
0.188857469
0
0
0.18850147
0
0
0.18814681
0
0.187793482
0
then the value of autocorrelations i.e. correlalogram comes as approx 
1
0.089252308
0.058227292
0.017934984
0.025264591
-0.014925678
-0.004668544
0.014890995
0.001625333
0.010669589
-0.010587179
-0.03000206
-0.011863654
0.00772247
0.024272208
-0.019521244
-0.035998575
-0.061608877
-0.048401231
-0.008594859

which show that the values are quite likely to make series stationary :)
> data[1:10,]     V1         V2
1  4.96  0.0000000
2  4.95 -0.2018164
3  4.96  0.2018164
4  4.96  0.0000000
5  4.97  0.2014099
6  4.97  0.0000000
7  4.97  0.0000000
8  4.97  0.0000000
9  4.97  0.0000000
10 4.98  0.2010051> adf.test(data[,1])
        Augmented Dickey-Fuller Test

data:  data[, 1] 
Dickey-Fuller = -1.1052, Lag order = 5, p-value = 0.9188
alternative hypothesis: stationary 
> adf.test(data[,2])
        Augmented Dickey-Fuller Test

data:  data[, 2] 
Dickey-Fuller = -6.2265, Lag order = 5, p-value = 0.01
alternative hypothesis: stationary 

Warning message:
p-value smaller than printed p-value in: adf.test(data[, 2])
> 
this explains everything clearly :)
your NULL hypothesis is "Series is not stationary" - hence hypothesis
in
negative sense

prooved by taking ideal data
> data1<-rnorm(10000) #normal data
> adf.test(data1)
        Augmented Dickey-Fuller Test

data:  data1 
Dickey-Fuller = -21.2118, Lag order = 21, p-value = 0.01
alternative hypothesis: stationary 

Warning message:
p-value smaller than printed p-value in: adf.test(data1)
> 
HTH




Megh Dal <megh700004@yahoo.com> 
Sent by: r-help-bounces@stat.math.ethz.ch
08/16/2007 04:27 PM

To
r-help@stat.math.ethz.ch
cc

Subject
[R] ADF test






Hi all,
 
  Hope you people do not feel irritated for repeatedly sending mail on 
Time series.
 
  Here I got another problem on the same, and hope I would get some answer 
from you.
 
  I have following dataset:
 
  data[,1]
  [1] 4.96 4.95 4.96 4.96 4.97 4.97 4.97 4.97 4.97 4.98 4.98 4.98 4.98 
4.98 4.99 4.99 5.00 5.01
 [19] 5.01 5.00 5.01 5.01 5.01 5.01 5.02 5.01 5.02 5.02 5.03 5.03 5.03 
5.03 5.03 5.04 5.04 5.04
 [37] 5.04 5.04 5.04 5.05 5.05 5.06 5.06 5.06 5.07 5.07 5.07 5.07 5.08 
5.07 5.08 5.08 5.09 5.10
 [55] 5.10 5.09 5.10 5.10 5.10 5.10 5.10 5.10 5.10 5.10 5.11 5.11 5.11 
5.11 5.11 5.11 5.11 5.12
 [73] 5.12 5.12 5.12 5.13 5.14 5.14 5.14 5.14 5.14 5.15 5.15 5.15 5.15 
5.14 5.15 5.15 5.15 5.16
 [91] 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.17 5.17 5.17 
5.17 5.17 5.18 5.18 5.18
[109] 5.18 5.18 5.19 5.19 5.20 5.20 5.20 5.20 5.20 5.21 5.21 5.21 5.21 
5.21 5.21 5.22 5.22 5.23
[127] 5.23 5.23 5.23 5.24 5.24 5.24 5.25 5.24 5.24 5.25 5.26 5.26 5.26 
5.26 5.26 5.26 5.26 5.27
[145] 5.27 5.26 5.27 5.27 5.28 5.29 5.29 5.29 5.29 5.30 5.30 5.30 5.31 
5.31 5.31 5.32 5.32 5.33
[163] 5.33

 
  Now I want to conduct a test for stationarity using ADF test :
 
  > adf.test((data[,1]), "stationary",  0)
          Augmented Dickey-Fuller Test
  data:  (data[, 1]) 
Dickey-Fuller = -3.7351, Lag order = 0, p-value = 0.02394
alternative hypothesis: stationary 

  But surprisingly it leads towards rejestion of NULL [p-value is less 
than 0.05], i.e. indicates a possible stationary series. However ploting a 
graph of actual data set it doesn't seem so.
 
  Am I making any mistakes ? Can anyone give me any suggestion?
 
  Regards,
  Megh

 
---------------------------------

                 [[alternative HTML version deleted]]

______________________________________________
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide 
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.



===========================================================================================DISCLAIMER
AND CONFIDENTIALITY CAUTION:\ \ This message and ...{{dropped}}

Leeds, Mark (IED)

2007-Aug-16 13:37 UTC

head link

[R] ADF test

The dickey fuller test results can change radically depending on how
many lags you use. If you don't use enough lags, then the 
regression assumptions won't hold and your test is not correct. If you
use too many lags, then you lose a lot of power. There is a lot 
of literature on how many lags to use. Schwert developed an algorthm and
it's in eric zivot's splus finmetrics text but I don't have that
book with me at the moment.  There's also the question of whether to
include a trend or intercept or both in your model. This issue
is discusssed very clearly in hamilton.




-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Megh Dal
Sent: Thursday, August 16, 2007 6:58 AM
To: r-help at stat.math.ethz.ch
Subject: [R] ADF test

Hi all,
   
  Hope you people do not feel irritated for repeatedly sending mail on
Time series.
   
  Here I got another problem on the same, and hope I would get some
answer from you.
   
  I have following dataset:
   
  data[,1]
  [1] 4.96 4.95 4.96 4.96 4.97 4.97 4.97 4.97 4.97 4.98 4.98 4.98 4.98
4.98 4.99 4.99 5.00 5.01  [19] 5.01 5.00 5.01 5.01 5.01 5.01 5.02 5.01
5.02 5.02 5.03 5.03 5.03 5.03 5.03 5.04 5.04 5.04  [37] 5.04 5.04 5.04
5.05 5.05 5.06 5.06 5.06 5.07 5.07 5.07 5.07 5.08 5.07 5.08 5.08 5.09
5.10  [55] 5.10 5.09 5.10 5.10 5.10 5.10 5.10 5.10 5.10 5.10 5.11 5.11
5.11 5.11 5.11 5.11 5.11 5.12  [73] 5.12 5.12 5.12 5.13 5.14 5.14 5.14
5.14 5.14 5.15 5.15 5.15 5.15 5.14 5.15 5.15 5.15 5.16  [91] 5.16 5.16
5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.16 5.17 5.17 5.17 5.17 5.17 5.18
5.18 5.18 [109] 5.18 5.18 5.19 5.19 5.20 5.20 5.20 5.20 5.20 5.21 5.21
5.21 5.21 5.21 5.21 5.22 5.22 5.23 [127] 5.23 5.23 5.23 5.24 5.24 5.24
5.25 5.24 5.24 5.25 5.26 5.26 5.26 5.26 5.26 5.26 5.26 5.27 [145] 5.27
5.26 5.27 5.27 5.28 5.29 5.29 5.29 5.29 5.30 5.30 5.30 5.31 5.31 5.31
5.32 5.32 5.33 [163] 5.33

   
  Now I want to conduct a test for stationarity using ADF test :
   
  > adf.test((data[,1]), "stationary",  0)
          Augmented Dickey-Fuller Test
  data:  (data[, 1])
Dickey-Fuller = -3.7351, Lag order = 0, p-value = 0.02394 alternative
hypothesis: stationary 

  But surprisingly it leads towards rejestion of NULL [p-value is less
than 0.05], i.e. indicates a possible stationary series. However ploting
a graph of actual data set it doesn't seem so.
   
  Am I making any mistakes ? Can anyone give me any suggestion?
   
  Regards,
  Megh

       
---------------------------------

	[[alternative HTML version deleted]]

______________________________________________
R-help at stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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This is not an offer (or solicitation of an offer) to buy/se...{{dropped}}

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