From: Horace Tso <Horace.Tso_at_pgn.com>

Date: Tue, 08 May 2007 11:17:36 -0700

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 08 May 2007 - 18:23:30 GMT

Date: Tue, 08 May 2007 11:17:36 -0700

Mark, I suppose you make the usual assumptions, ie. E[x]=0, E[x*epsilon]=0, the correlation is just simply,

corr(x,y) = beta * ( var(x) / var(y) )

And you could get var(y) from var(x) and var(epsilon).

**HTH.
**
Horace

>>> "Leeds, Mark (IED)" <Mark.Leeds_at_morganstanley.com> 5/8/2007 10:25:11 AM >>>

This is not an R question but if anyone can help me, it's much
appreciated.

Suppose I have a series ( stationary ) y_t and a series x_t ( stationary )and x_t has variance sigma^2_x and epsilon is normal (0, sigma^2_epsilon )

and the two series have the relation

y_t = Beta*x_t + epsilon

My question is if there are particular values that sigma^2_x and sigma^2_epsilon have to take in order for corr(x_t,y_t) to equal Beta ?

I attempted to figure this out using two different methods and in one case I end up involving sigma^2_epsilon and in the other I don't and I'm not sure if either method is correct. I think I need to use results form the conditional bivariate normal but i'm really not sure. Also, it's not a homework problem because I am too old to have homework. Thanks for any insights/solutions.

This is not an offer (or solicitation of an offer) to buy/se...{{dropped}}

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 08 May 2007 - 18:23:30 GMT

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