On Sun, 22 Nov 2009, zubin wrote:
Hello, can one use predict, as you can with other model objects like lm,
with dynlm to predict a new data set that is identical in field names,
just a different time period.
Be nice if you could, I don't really want to create a new data set with
all the lags, hoping it would generate dynamically. Does not seem to
work, get a # of column error. Any suggestions?
Currently, this is not possible, yet. It would indeed be nice if dynlm()
could figure out which of the variables are determined by the response and
predict these recursively, while the remaining variables would need to be
provided (or even updated by auxiliary AR models). But currently, it's not
clear to me how all these cases should be resolved exactly and how the
additional formula parsing should be done, so it's not implemented yet.
Sorry...
Z
R> str(dfz)
An 'xts' object from 2009-09-25 09:45:06 to 2009-10-19 15:00:57 containing:
Data: num [1:28232, 1:8] 0.54771 -0.00825 1.27406 0.69705 1.08107 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:8] "PC1" "PC2" "PC3" "PC4" ...
Indexed by objects of class: [POSIXt,POSIXct] TZ: GMT
xts Attributes:
NULL
R> str(z)
An 'xts' object from 2009-10-21 09:45:04 to 2009-10-21 15:00:56 containing:
Data: num [1:2304, 1:8] -0.5044 1.237 -0.7764 0.3931 0.0629 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:8] "PC1" "PC2" "PC3" "PC4" ...
Indexed by objects of class: [POSIXt,POSIXct] TZ: GMT
xts Attributes:
NULL
dols = dynlm(FAS0 ~ L(FAS0,1:10) + L(PC1,0:10) + L(PC2,0:10) +
L(PC3,0:10) + L(PC4,0:10) + L(PC5,0:10) + L(PC6,0:10) + L(PC7,0:10),
data=dfz)
R> predict(dols,newdata=z)
/*Error in fix.by(by.x, x) : 'by' must match numbers of columns*/
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