[R] Time series misalignment

[Fernando Saldanha]

Thanks, Achim,

I managed to do what I wanted, thanks to your suggestion, except for
one thing. When I called ts.intersect I could only provide numerical
arguments (more precisely, objects that can be coerced into time
series, I guess). That means I was not able to pass the original
row.names that I had read into a data frame (those were character
strings). At that point those row.names became misaligned with the
data frame created by the ts.intersect call, whose row names were just
1, 2, 3, .... Is there a way to avoid this problem?

I will check the zoo package, but I started with R three days ago, so
it's a bit of information overload right now.

Thanks for the help.

FS

On 4/12/05, Achim Zeileis <Achim.Zeileis at wu-wien.ac.at> wrote:
> On Tue, 12 Apr 2005 18:47:21 -0400 Fernando Saldanha wrote:
> 
> > Can one also predetermine a set and then estimate all the models one
> > wants to compare using the zoo package?
> 
> Sure, you can merge() several series first and then pass this as the
> data argument to lm(). See the vignette of the zoo package for more
> examples.
> 
> > Or can that be done only with the tseries package?
> 
> Really, you are *not* using the tseries package here!
> 
> (In the old days, the class "ts" and its methods used to be in the
> package ts, but this was merged into stats long ago. tseries is an
> entirely different package.)
> Z
> 
> > Thanks.
> >
> > FS
> >
> > On 4/12/05, Achim Zeileis <Achim.Zeileis at wu-wien.ac.at> wrote:
> > > Fernando:
> > >
> > > > This maybe a basic question, but I have spent several hours
> > > > researching and I could not get an answer, so please bear with me.
> > > > The problem is with time series in the package tseries.
> > >
> > > BTW: the `tseries' package is not involved here.
> > >
> > > > As the example
> > > > below shows, the time series can get misaligned, so that bad
> > > > results are obtained when doing regressions.
> > >
> > > lm() per se has only very limited support for time series
> > > regression. Therefore, there are currently several tools under
> > > development for addressing this issue. In particular, Gabor
> > > Grothendieck and myself are working on different approaches to this
> > > problem.
> > >
> > > <snip>
> > >
> > > > To fix this problem I did the following:
> > > >
> > > > > tsf <- ts.intersect(y1, x1, z1)
> > > >
> > > > Now I can do:
> > > >
> > > > > lm1 <- lm(tsf[,3] ~ tsf[,2])
> > >
> > > It is probably simpler to just do
> > >   lm1 <- lm(z1 ~ x1, data = tsf)
> > >
> > > Another approach is implemented in the zoo package. This implements
> > > an formula dispatch and you can do
> > >   lm1 <- lm(I(z1 ~ x1))
> > > *without* computing tsf first.
> > >
> > > Depending on what you want to do with the fitted models, one of the
> > > two approaches might be easier, currently. In particular, if you
> > > want to fit and compare several models, then I would compute the
> > > intersection first and fit all models of interest on this data set.
> > >
> > > Furthermore, note that the dispatch implementation via I() in zoo is
> > > still under development and likely to change in future versions.
> > > (But this mainly means that improved implementations will become
> > > available soon, stay tuned :-)
> > > Z
> > >
> > >
> >
>