'time' was a trend variable from my.data set. Equivalent to the output of
the command 'matrix' you just gave me.
So now I did:
library(urca)
data(my.data)
names(my.data)
attach(my.data)
dat1 <- my.data[, c("dY", "X", "dM")]
mat1 <- matrix(seq(1:nrow(dat1)), ncol = 1)
args('ca.jo')
yxm.vecm <- ca.jo(dat1, type = "trace", ecdet = "const", K = 2, spec =
"longrun", dumvar=mat1)
and the output is:
Error in r[i1, , drop = FALSE] - r[-nrow(r):-(nrow(r) - lag + 1L), , drop =
FALSE] :
non-numeric argument to binary operator
In addition: Warning message:
In ca.jo(dat1, type = "trace", ecdet = "const", K = 2, spec = "longrun", :
No column names in 'dumvar', using prefix 'exo' instead.
What do I do wrong?
Best,
Greg
2011/3/31 Pfaff, Bernhard Dr. <bernhard_pf...@fra.invesco.com>
>
>
>
> Hello Bernhard,
>
> thank You so much one again! Now I (more or less) understand the idea, but
> still have problem with its practical application.
>
> I do (somewhat following example 8.1 in your textbook):
>
> library(urca)
> data(my.data)
> names(my.data)
> attach(my.data)
> dat1 <- my.data[, c("dY", "X", "dM")]
> dat2 <- cbind(time)
>
> What is 'time'? Just employ matrix(seq(1:nrow(dat1)), ncol = 1) for
> creating the trend variable.
>
> Best,
> Bernhard
>
>
> args('ca.jo')
> yxm.vecm <- ca.jo(dat1, type = "trace", ecdet = "trend", K = 2, spec =
> "longrun", dumvar=dat2)
>
> The above code produces following output:
>
> Error in r[i1, , drop = FALSE] - r[-nrow(r):-(nrow(r) - lag + 1L), , drop
> = FALSE] :
> non-numeric argument to binary operator
>
> What does that mean? Should I use cbind command to dat1 as well? And
> doesn't it transform the series into series of integer numbers?
>
> Thank you once again (especially for your patience).
>
> Best,
> Greg
>
>
>
> 2011/3/31 Pfaff, Bernhard Dr. <bernhard_pf...@fra.invesco.com>
>
>> Hello Greg,
>>
>> you include your trend as a (Nx1) matrix and use this for 'dumvar'. The
>> matrix 'dumvar' is just added to the VECM as deterministic regressors and
>> while you are referring to case 5, this is basically what you are after, if
>> I am not mistaken. But we aware that this implies a quadratic trend for the
>> levels.
>>
>> Best,
>> Bernhard
>>
>> ------------------------------
>> *Von:* Grzegorz Konat [mailto:grzegorz.ko...@ibrkk.pl]
>> *Gesendet:* Mittwoch, 30. März 2011 20:50
>> *An:* Pfaff, Bernhard Dr.; r-help@r-project.org
>> *Betreff:* Re: [R] VECM with UNRESTRICTED TREND
>>
>> Hello Bernhard,
>>
>> Thank You very much. Unfortunately I'm still not really sure how should I
>> use dummy vars in this context...
>> If I have a system of three variables (x, y, z), lag order = 2 and 1
>> cointegrating relation, what should I do? I mean, what kind of 'pattern'
>> should be used to create those dummy variables, what should they represent
>> and how many of them do I need?
>>
>> Many thanks in advance!
>>
>> Best,
>> Greg
>>
>> 2011/3/30 Pfaff, Bernhard Dr. <bernhard_pf...@fra.invesco.com>
>>
>>> Hello Greg,
>>>
>>> you can exploit the argument 'dumvar' for this. See ?ca.jo
>>>
>>> Best,
>>> Bernhard
>>>
>>> > -----Ursprüngliche Nachricht-----
>>> > Von: r-help-boun...@r-project.org
>>> > [mailto:r-help-boun...@r-project.org] Im Auftrag von Grzegorz Konat
>>> > Gesendet: Mittwoch, 30. März 2011 16:46
>>> > An: r-help@r-project.org
>>> > Betreff: [R] VECM with UNRESTRICTED TREND
>>> >
>>> > Dear All,
>>> >
>>> > My question is:
>>> >
>>> > how can I estimate VECM system with "unrestricted trend" (aka
>>> > "case 5") option as a deterministic term?
>>> >
>>> > As far as I know, ca.jo in urca package allows for "restricted trend"
>>> > only [vecm
>>> > <- ca.jo(data, type = "trace"/"eigen", ecdet = "trend", K =
>>> > n, spec = "transitory"/"longrun")].
>>> > Obviously, I don't have to do this in urca, so if another
>>> > package gives the possibility, please let me know too!
>>> >
>>> > Thanks in advance!
>>> >
>>> > Greg
>>> >
>>> > [[alternative HTML version deleted]]
>>> >
>>> > ______________________________________________
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>>> > PLEASE do read the posting guide
>>> > http://www.R-project.org/posting-guide.html
>>> > and provide commented, minimal, self-contained, reproducible code.
>>> >
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>>
>
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