R help - Apr 2005 - Time series misalignment

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. As the example
below shows, the time series can get misaligned, so that bad results
are obtained when doing regressions. I found a way to do this
correctly, but I find it rather cumbersome. My question is: is there a
better way to do it?

Thanks for any help.

Suppose I define:
> y1<-ts(c(1,2,3,5,7))
> x1<-diff(y1)
> z1<-ts(c(1,1,2,2))
Then I get:
> x1
Time Series:
Start = 2 
End = 5 
Frequency = 1 
[1] 1 1 2 2
> z1
Time Series:
Start = 1 
End = 4 
Frequency = 1 
[1] 1 1 2 2

Notice that the Start values for x1 and z1 are different.

However, if I regress z1 on z1 I get:
> reg1 <- lm(z1 ~ x1, na.action = NULL)
> reg1
Call:
lm(formula = z1 ~ x1, na.action = NULL)

Coefficients:
(Intercept)           x1  
          0            1  
          
But this is the wrong answer. The time series z1 and x1 are
misaligned. lm is ignoring the fact that Start = 2 for x1 and Start 1 for z1.

To fix this problem I did the following:
> tsf <- ts.intersect(y1, x1, z1)
> tsf
Time Series:
Start = 2 
End = 4 
Frequency = 1 
  y1 x1 z1
2  2  1  1
3  3  1  2
4  5  2  2

These versions of z1 and x1 are correctly aligned.

Now I can do:
> lm1 <- lm(tsf[,3] ~ tsf[,2])
> lm1
Call:
lm(formula = tsf[, 3] ~ tsf[, 2])

Coefficients:
(Intercept)     tsf[, 2]  
        1.0          0.5 
        
This is the correct answer. However, it is rather cumbersome to refer
to the aligned variables as columns of the time series object tsf.

As an observation, I also called ts.intersect with the option dframe t and got
exactly the same results.

So my question is: is there a less cumbersome way to keep these time
series aligned?

Thanks again for any help.

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