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Re: st: MA(1) process
From
Robert A Yaffee <[email protected]>
To
[email protected]
Subject
Re: st: MA(1) process
Date
Fri, 09 Jul 2010 11:50:15 -0400
Ari,
If you assume that your mean-centered series is a function of its past observations, it has an AR structure. AR(1) means that it is a function of
only the first lag of itself. With an MA(1) structure, the observation is a function of the current and first lag of the disturbance (shock, innovation or error).
You can convert one to the other. Actually, an AR(1) is functionally equivalent to a MA("infinite") and an AR("infinite") is functionally equivalent to a MA(1), assuming covariance stationarity.
You may first need to test before you make these assumptions.
- 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: Ari Dothan <[email protected]>
Date: Friday, July 9, 2010 11:32 am
Subject: st: MA(1) process
To: [email protected]
> Hi Statalisters,
> I am using a gmm procedure for dynamic panels which makes it possible
> to fit a model with an MA(1) error structure (moving average (1st
> order). Most other procedures, such as fixed effects, use the AR(1)
> error structure.
> Could anyone explain me in layman’s terms what is the difference
> between the MA(1) and the AR(1) error structures? Why, and when,
> should one be used rather than the other?
> Thanks
> --
> Ari Dothan
>
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