dear useRs (especially andrew and gabor),
you have helped me a lot, the ar(1)/ornstein-uhlenbeck type is exactly
it (0
thank you,
josuah
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PLEASE do read the posting gu
Autoregression is more general than the (discretized) Ornstein
Uhlenbeck process. For a start, a discretized version of the Ornstein-
Uhlenbeck is just an AR(1) process X(n+1) = X(n) + a (X(n) - mu) +
error(n+1), but with the coefficient a restricted to 0 < a < 1. This
restriction is necessary as
i think there's confusion here between a time series that reverts to its
long term mean
and an "ornstein uhlenbeck" type of mean reversion. they're not the same
thing and
I don't want to go into the difference because I would probably just add
to the confusion.
you might be better off sending
Autoregression is just X(n+1) = a X(n) + b + error. The mean
reverting model is when |a| < 1. Estimation is carried out using
x_ar <- ar(x)
summary(x_ar)
standard error is found in the square root of the diagonal of the x_ar
$asy.var.coef matrix.
please read the documentation found at ?ar to g
hi andrew,
the problem is that I don't know what kind of model this exactly is...
I only know that I have to do it this way and how the model is
structured.
Mean reverting model = autoregression? If so, then search for
?ar
or
?arima
to fit a time series.
On Mar 10, 4:36 am, Josuah Rec
Mean reverting model = autoregression? If so, then search for
?ar
or
?arima
to fit a time series.
On Mar 10, 4:36 am, Josuah 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 cons
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
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