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Re: st: MA(1) process
Ari Dothan <email@example.com>
Re: st: MA(1) process
Fri, 9 Jul 2010 19:10:31 +0300
Thanks very much, Professor Yaffee. This is now much clearer
On Fri, Jul 9, 2010 at 6:50 PM, Robert A Yaffee <firstname.lastname@example.org> wrote:
> 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@example.com>
> Date: Friday, July 9, 2010 11:32 am
> Subject: st: MA(1) process
> To: firstname.lastname@example.org
>> 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?
>> Ari Dothan
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