Thank you for your post Greg.
Do you have any useful references regarding this variability (papers etc)?
 
Many thanks.
Dave

From: Greg Snow <Greg.Snow@imail.org>
To: Dave Evens <daveevens1@yahoo.co.uk>; "r-help@r-project.org" <r-help@r-project.org>
Sent: Thursday, 16 June 2011, 21:32
Subject: RE: [R] prediction intervals

I don't think that this approach is appropriate here.  Each iteration after the 1st the lm/predict combination will assume that the new data is exact when in fact it is an estimate with some error involved.  To properly do this you need to take into account that variability.  There is a time series task view on CRAN that may point you to better tools.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow@imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Dave Evens
> Sent: Thursday, June 16, 2011 11:33 AM
> To: r-help@r-project.org
> Subject: [R] prediction intervals
> 
> 
> 
> Dear members,
> 
> I'm fitting linear model using "lm" which has numerous auto-regressive
> terms as well as other explanatory variables. In order to calculate
> prediction intervals, i've used a for-loop as the auto-regressive
> parameters need to be updated each time so that a new forecast and
> corresponding prediction interval can be calculated.
> 
> I'm fitting a number of these models which have different values for
> the response variable and possibly different explanatory variables. The
> response is temperature in fahrenheit (F), and the different models are
> for cities. So each city has its own fitted linear model for
> temperature. I'm assuming that they're independent models for the time
> being, I want to combine the results across all cities and have overall
> prediction intervals. Because I assuming that they're independent can I
> just add together the degrees of freedom from each model (i.e. total
> degrees of freedom=df1+df2+...) and the variance-covariance matrices
> (i.e. V=V1+V2+...) in order to calcalate the overall prediction
> intervals?
> 
> Any help would be most appreciated.
> 
> Regards,
> Dave
>     [[alternative HTML version deleted]]
> 
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