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re: st: Question on a quasi-time series
From
"Ariel Linden, DrPH" <[email protected]>
To
<[email protected]>
Subject
re: st: Question on a quasi-time series
Date
Wed, 23 Mar 2011 08:43:32 -0700
It seems to me that time series analysis is not the appropriate approach
here. If there are only a couple of monthly observations available for each
year of data, I cannot imagine how the model will fit the missing 10 months
in any accurate way.
I would consider comparing only the observations for "like" months in each
year so that you are limiting the evaluation to data you actually have.
Similarly, you'd have a good rationale for limiting the analysis to only
those months of observation if indeed no other observations occur outside
the timeframe.
As to which statistical approaches to take, I think that perhaps a
difference-in-differences approach could be considered, or perhaps some type
of correlation approach (i.e., intra-class, controlling for within year
serial dependency)
Just some food for thought...
Ariel
Date: Tue, 22 Mar 2011 21:27:45 +0000
From: rachel grant <[email protected]>
Subject: st: Question on a quasi-time series
Hi
I have daily count data over a number of years totalling 317 cases.
However it's not a true time series because I have not got a full year for
each year, just a month or two.
The data are likely to be partially serially dependent within years but not
between years. So I am not sure how to correct for the possible serial
dependency.
What I have tried is ranking the data 1-317 and then using this ranking to
use the command "tsset". Will this work? Alternatively should I simply
increase my confidence level to p= 0.01 and not bother trying to correct for
the autocorrelation? Thanks!
- --
regards, Rachel Grant
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------------------------------
Date: Tue, 22 Mar 2011 21:39:56 +0000
From: Nick Cox <[email protected]>
Subject: st: RE: Question on a quasi-time series
I wouldn't tackle it that way at all. Ranking values has nothing to do with
time or dependence structure! Time series problems are rarely such that you
can just tweak a critical level!
You have a time series, just one with lots of missing data. You can still
-tsset- on daily date and look at e.g. the autocorrelation function. You can
still look for trends.
The bigger question is why you have gaps. If the gappiness is capricious as
far as the phenomena are concerned, that is the best news. On the other
hand, it seems far more likely that either the organisms or the observers
were visible or active in the field at certain times, e.g. seasonality for
the organisms or it was research time with good weather and light and
absence of teaching or committee work for the observers.
(I am interpolating here a memory that your data are essentially ecological;
I may be misremembering, or this problem may be different. Either way, just
abstracting a problem from its context often obscures it and makes good
advice more difficult.)
Nick
[email protected]
rachel grant
I have daily count data over a number of years totalling 317 cases.
However it's not a true time series because I have not got a full year for
each year, just a month or two.
The data are likely to be partially serially dependent within years but not
between years. So I am not sure how to correct for the possible serial
dependency.
What I have tried is ranking the data 1-317 and then using this ranking to
use the command "tsset". Will this work? Alternatively should I simply
increase my confidence level to p= 0.01 and not bother trying to correct for
the autocorrelation? Thanks!
*
* 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/