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- From: hauser@statrix2.wu-wien.ac.at (Michi Hauser)
- Newsgroups: sci.math.stat
- Subject: Re: generating correlated random numbers
- Date: 5 Nov 1992 22:53:01 GMT
- Organization: Wirtschaftsuniversitaet Wien
- Lines: 44
- Distribution: all
- Message-ID: <1dc8kdINNt1q@nestroy.wu-wien.ac.at>
- References: <JOHNS.92Oct24163602@haggis.ccsf>
- NNTP-Posting-Host: statrix2.wu-wien.ac.at
-
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- You want a long series with a given autocorrelation structure.
-
- Low order ARMA models are used to model the autocorrelation
- structure of weakly stationary processes.
-
- (1- a_1L - a_2L^2 - ... - a_pL^p) x_t =
- (1 - b_1L - b_2L^2 - ... - b_qL^q) u_t
-
- u_t ... white noise
-
- If you reverse the procedure and start with a special choice
- of the parameters you obtain a certain covariance structure.
- Cooose the parameters so that you end up with the desired C matrix.
- You find the formula in any times series book.
-
- MA(q) processes have a C matrix with a (2q+1)-band structure,
- AR(1) processes an infinte matrix with geometrically decreasing
- coefficients (c_i.) .
- The actual generation by
-
- x_t = a_1 x_(t-1) + ... + a_p x_(t-p) -
- - u_t - b_1 u_(t-1) - ... - b_q u_(t-q)
-
- needs starting values for x_(t-i) ,i=1,...,p and some u_(t-j) .
- The latter are white noise and no problem.
-
- The effect of the x`s, however, should be neglegible.
- How quickly this starting value effect dies out depends on the
- roots of the AR polynomial:
- roots close to the unit circle take a long time,
- roots far away of the unit circle ( |z| >> 1 ) take only a short time.
-
- E.g. AR(1): corr( x_t,x_(t-i) )= a_1^i
- ( 0.1^10 = very small, 0.9^10=0.349 ! )
-
- Remark:
- For MA-type correlation structures there is no need to factorize
- the band matrix this method is equally exact.
-
- Best wishes Michael
-
- email: hauser@wu-wien.ac.at
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