[R] correcting for autocorrelation in models with panel data?

[Prof Brian Ripley]

Are the years equally spaced and in time order?
If so, it probably doesn't matter, and if not you may want corCAR1 not 
corAR1.


On Thu, 10 Feb 2005, David Hugh-Jones wrote:

> Assuming I have years in YEAR and state ids in ID, I guess the
> correlation ought to be
>
> corAR1(form = ~ YEAR | ID)
>
> ?
>
> Thanks a lot,
> David
>
>
>
>
> On Thu, 10 Feb 2005 12:36:32 -0500, Doran, Harold <HDoran at air.org> wrote:
>> In the nlme package you can find the gls() function to account for
>> autocorrelation over time using corAR1. Syntax might look something like
>> this:
>>
>> fm1 <- gls(response ~ IV, long, correlation=corAR1(form=~1|ID),
>> method='ML')
>>
>> You can also use weights() for heteroscedasticity.
>>
>> -Harold
>>
>> -----Original Message-----
>> From: r-help-bounces at stat.math.ethz.ch
>> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of David Hugh-Jones
>> Sent: Thursday, February 10, 2005 12:15 PM
>> To: r-help at stat.math.ethz.ch
>> Subject: [R] correcting for autocorrelation in models with panel data?
>>
>> Hi
>>
>> I have some panel data for the 50 US states over about 25 years, and I
>> would like to test a simple model via OLS, using this data. I know how
>> to run OLS in R, and I think I can see how to  create Panel Corrected
>> Standard Errors using
>>
>> http://jackman.stanford.edu/classes/350C/pcse.r
>>
>> What I can't figure out is how to correct for autocorrelation over time.
>> I have found a lot of R stuff on time series models but they all seem
>> focused on predicting a single variable from its previous values.
>> Can anyone explain to me how to detect and get round autocorrelation?
>> Is there a package for panel data that I have missed?
>>
>> I appreciate that this is probably just as much about my ignorance of
>> econometrics as about R itself!
>>
>> Cheers
>> David

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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