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Re: st: MA(1) process


From   Ari Dothan <ari.dothan@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: MA(1) process
Date   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 <bob.yaffee@nyu.edu> wrote:
> 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 <ari.dothan@gmail.com>
> Date: Friday, July 9, 2010 11:32 am
> Subject: st: MA(1) process
> To: statalist@hsphsun2.harvard.edu
>
>
>> 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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>



-- 
Ari Dothan

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