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Say I have a VAR(p) model without any noise (a multidimensional AR model without noise). How would I go about calculating the coefficients that are MSE optimal? Is there an extension to the Yule Walker equations for multidimensional data?

Edit: Maybe an equation like the following would help?

$\hat Y_{k+1}^{}=AY_{k} $

where any general $\ Y_{i}=[Y_{1} \ Y_{2}\ Y_{3}\ Y_{4}\ ...... \ Y_{p}]^{T} $ and I want to estimate the optimal A in MSE sense. Such a problem should fit under a Weiner filtering approach in my opinion. Would using the matrix orthogonality principle be sufficient to solve this?

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    $\begingroup$ Contrary to what you are implying, an AR model does contain a noise component. E.g. in an AR(1): $y_t=c+\varphi_1 y_{t-1}+\varepsilon_t$, it is $\varepsilon_t$. Given that, can you specify more precisely what your VAR(p) model without any noise is? $\endgroup$ May 13 at 7:55
  • $\begingroup$ @RichardHardy I updated my question and made it clearer. $\endgroup$
    – Harduin
    May 14 at 3:34

1 Answer 1

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$Y_{k+1}=AY_{k}$ where any general $\ Y_{i}=[Y_{1} \ Y_{2}\ Y_{3}\ Y_{4}\ ...... \ Y_{p}]^{T} $ and I want to estimate the optimal A in MSE sense.

So you have an exact, deterministic, linear relationship. The only thing that is missing is the coefficient values in $A$. Then equation-by-equation OLS will return you exact estimates, and they will be MSE-optimal.

I am trying to figure out a problem where this is perhaps not even $ Y_{k+1}$ but rather maybe just an estimate of it.

If you change your problem by making $Y_{k+1}$ only observable with some error while $Y_1,\dots,Y_k$ are observable without error, you get a multivariate multiple regression. There is no MSE-optimal estimator for that. If you were to restrict your attention to linear estimators and the error variance were given, it would be a form of ridge regression (a univariate case is discussed in Dave Giles' blog "A Regression "Estimator" that Minimizes MSE". But that is perhaps too restrictive.

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  • $\begingroup$ Why is there no MSE optimal answer for that? Shouldnt the Orthogonality Principle result in a system of equations that can solve this? I have a picture of my approach but I am unsure whether a non latex solution would be appropriate on this forum. $\endgroup$
    – Harduin
    May 14 at 19:31
  • $\begingroup$ @Harduin, I am not familiar with the orthogonality principle from before, but from a casual read it seems it might ignore estimation imprecision and so would not be directly applicable in practice. Have you checked out the blog post? It deals with these issues. $\endgroup$ May 14 at 19:36
  • $\begingroup$ And since you are using signal processing terminology, you may be interested in learning there is Signal Processing Stack Exchange. $\endgroup$ May 14 at 19:50
  • $\begingroup$ Thank you for the help. I'll look into this. $\endgroup$
    – Harduin
    May 15 at 0:49

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