If you are serious about doing seasonal test, the best option is to use the
specialized softwares. They will have all the options for you to test. Doing
it as add-in, in my opinion, is not good because you will not be able to
access the latest techniques. There's a lot of other things you need to take
care before the seasonal test -- outliers, trading days, moving holidays...
I would suggest a very simple to use interface that includes both x12-ARIMA
and SEATS/TRAMO methods -- Demetra from Eurostat.
Best,
Ky Tran
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of kelly johnson
Sent: Friday, April 01, 2005 11:42 AM
To: [email protected]
Subject: RE: st: RE: seasonality
hi nick,
thank you for your reply. i express my seasonality question a bit clearer.
suppose we are looking at five years of stock retruns data. how can we test
for teh presence of seasonality in specific months?
if i were to do the typical adjustment, i'd first calculated the average
over teh entire time period, and divide my returns by that average. call the
new variable rtn_adj. then i would have to average, by month, to get the
seasonl adjsutement indices for each month (which should sum up to 12). i
can then retruns and multiply teh original data by each of the appropriate
seasonal adjsutemtn factor. My question: is there a simpler was to do this?
also, how can i do f-test to test teh presence of seasonality? to give youa
better idea, i'm attaching a sample of the data to give you an idea what i'm
looking at.
regards
kelly
Date Returns
December-04 0.072894168
November-04 0.035794183
October-04 0.084951456
September-04 -0.02658003
August-04 0.030641234
July-04 -0.079050645
June-04 -0.04955595
May-04 0.050373134
April-04 -0.08951928
March-04 -0.026942149
February-04 0.044003451
January-04 0.002421726
December-03 0.033428674
November-03 -0.038666438
October-03 0.105853288
September-03 -0.00472858
August-03 -0.007695195
July-03 0.164844775
June-03 0.078010841
May-03 0.058632735
April-03 0.159722222
March-03 0.03877367
February-03 -0.022333235
January-03 -0.077277657
December-02 -0.127513603
November-02 0.150830384
October-02 0.151771715
September-02 -0.09015691
August-02 0.015942029
July-02 -0.115838032
June-02 -0.005353046
May-02 -0.025341615
April-02 -0.242709313
March-02 0.154932638
February-02 -0.056193601
January-02 -0.021865597
December-01 0.04049259
November-01 0.14589811
October-01 0.080640993
September-01 -0.21329808
August-01 -0.045789678
July-01 -0.084547069
June-01 -0.088111435
May-01 0.058097686
April-01 0.111005331
March-01 -0.075026418
February-01 -0.173868762
January-01 0.065581395
December-00 0.178082192
November-00 -0.171207993
October-00 0.060523358
September-00 -0.08892789
August-00 0.126730389
July-00 0.123911836
June-00 0.166090713
May-00 -0.02690206
April-00 -0.029177719
March-00 0.024456522
February-00 0.19272002
January-00 0.041277259
>From: "Nick Cox" <[email protected]>
>Reply-To: [email protected]
>To: <[email protected]>
>Subject: st: RE: seasonality
>Date: Fri, 1 Apr 2005 16:57:48 +0100
>
>Welcome back after your hiatus.
>
>To centre a variable you need to go
>
>su y, meanonly
>gen yc = y - r(mean)
>
>except that this is such a common need
>that users have created commands to
>do that for you. For example, look at Ben Jann's
>-center- on SSC:
>
>. ssc desc center
>
>Your question about seasonality is much
>fuzzier. There are lots of different ways
>of testing for seasonality. In the
>environmental sciences, I would usually
>try fitting sine and cosine terms given
>2 * pi * (position in year / length of year).
>That is, also, I guess a congenial approach
>for most natural scientists.
>
>With economic or social data other methods appear
>more common, and may or may not be more
>appropriate. People seem happier with looking
>at lags 4, 12, whatever depending on whether
>data are quarterly, monthly, whatever. That may
>be what SAS command proc x12 does. But
>the adjustments seem much more complicated
>given complications like holidays that are
>irrelevant outside the human sphere.
>
>In any case, graphics are often useful
>for getting a handle on seasonality and
>often surprising neglected by people like
>economists.
>
>Without knowing more about your data
>or your research problem this is rather too
>large a question to answer well. In short, the
>question may be quick but the answer isn't.
>
>Nick
>[email protected]
>
>kelly johnson
>
> > I am returning to stata after a hatus of a couple of years. I
> > had two quick questions:
> >
> > Suppose I am using a stream of time seies data for a single
> > varible (Data, Variable1):
> >
> > (1) suppose i wanted to generate a new varible that equale
> > variable1 - the
> > mean of varible1. how do i do this (without having to create
> > a whole column
> > with only the mean of variable1 in it)?
> >
> > (2) is there an easy wasy to test for seasonality? in sas we
> > have the proc
> > x12 command? what's a quickand eqasy way to test for
> > seasonality in data?
>
>
>*
>* For searches and help try:
>* http://www.stata.com/support/faqs/res/findit.html
>* http://www.stata.com/support/statalist/faq
>* http://www.ats.ucla.edu/stat/stata/
_________________________________________________________________
Express yourself instantly with MSN Messenger! Download today - it's FREE!
http://messenger.msn.click-url.com/go/onm00200471ave/direct/01/
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
--
No virus found in this incoming message.
Checked by AVG Anti-Virus.
Version: 7.0.308 / Virus Database: 266.8.6 - Release Date: 3/30/2005
--
No virus found in this outgoing message.
Checked by AVG Anti-Virus.
Version: 7.0.308 / Virus Database: 266.8.6 - Release Date: 3/30/2005
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
