Message-ID: <22430366.post@talk.nabble.com>
Date: 2009-03-10T09:10:05Z
From: Christofer Bogaso
Subject: re[R-sig-finance] gression problem
In-Reply-To: <26BA4E97-112F-461D-9728-ADE02FC14B25@bgki.net>
You might try MLE, construct the liklihood function and then optimize it by
experimenting different choices of parameters. I have doubt how LS
estimation procedure can be employed here as parameters are nonlinear in
nature
rechtsteiner wrote:
>
> dear useRs,
>
> i'm working with a mean reverting model of the following specification:
>
> y = mu + beta(x - mu) + errorterm, where mu is a constant
>
> currently I estimate just y = x (with lm()) to get beta and then
> calculate mu = estimated intercept / (1-beta).
>
> but I'd like to estimate mu and beta together in one regression-step
> and also get the test-statistics (including parameter variance) for mu
> as well as for beta in the summary of the regression.
>
> could you please help me?
>
> thanks very much in advance!
>
> josuah
>
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