The code you gave me works fine with Finland, but the same for my data -
does not!
I do:
library(urca)
data(my.data)
dat1 <- my.data[, c("dY", "X", "dM")]
trend <- matrix(1:nrow(dat1), ncol = 1)
colnames(trend) <- "trd"
yxm.vecm <- ca.jo(dat1, type = "trace", ecdet = "const", K = 2, spec =
"longrun", dumvar = trend)
and the result is again:
Error in r[i1, , drop = FALSE] - r[-nrow(r):-(nrow(r) - lag + 1L), , drop =
FALSE] :
non-numeric argument to binary operator
I attach my dataset in xls format. If you have 5 minutes and wish to check
it out, I'd be extremely grateful!
Best,
Greg
2011/3/31 Pfaff, Bernhard Dr. <bernhard_pf...@fra.invesco.com>
> Well, without further information, I do not know, but try the following
>
> library(urca)
> example(ca.jo)
> trend <- matrix(1:nrow(sjf), ncol = 1)
> colnames(trend) <- "trd"
> ca.jo(sjf, type = "trace", ecdet = "const", K = 2, spec = "longrun",
> dumvar = trend)
>
> Best,
> Bernhard
>
>
>
> ------------------------------
> *Von:* Grzegorz Konat [mailto:grzegorz.ko...@ibrkk.pl]
> *Gesendet:* Donnerstag, 31. März 2011 14:40
>
> *An:* Pfaff, Bernhard Dr.; r-help@r-project.org
> *Betreff:* Re: [R] VECM with UNRESTRICTED TREND
>
> '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]]
>>>> >
>>>> > ______________________________________________
>>>> > R-help@r-project.org mailing list
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>>>> > http://www.R-project.org/posting-guide.html
>>>> > and provide commented, minimal, self-contained, reproducible code.
>>>> >
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>>
>
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