[R-SIG-Finance] Forcasting VAR/VEC
Brian G. Peterson
brian at braverock.com
Fri Dec 17 17:35:43 CET 2010
On 12/17/2010 09:37 AM, Megh Dal wrote:
> Thanks Mat for your reply. However this is not what I wanted. I already have the coefficients estimated and using those estimated coefficients I want to forecast. Here your input is TS data, from that data you have estimated coefficients, which is of specific class. Therefore you could use directly predict() function.
Not what you wanted is different from what you asked, which was how to
predict a VAR/VEC model. Bernhard and Mat have given you an answer
which is not what you wanted to hear, which seems to be that someone
else would magically have invented the same custom data format that you
choose to use?
> As I have coefficients in Matrix class, I cant convert them to specific class for prediction. Is there any way to do that?
Then you will likely have to modify the predict() function to either
take the coefficients directly, or you will have to take your custom
non-package-compliant Matrix and format it into a vars object like that
that predict() is .
see
?vars::VAR
for the format of the 'varest' list to give you input on what you will
need to do to transform your data so that it will work with predict()
> 2ndly I wondering how without the initial values i.e. last few observations of TS, predict() function in your example could actually predict?
You need the data to make a prediction. Period.
- Brian
> --- On Thu, 12/16/10, mat<matthieu.stigler at gmail.com> wrote:
>
>> From: mat<matthieu.stigler at gmail.com>
>> Subject: Re: [R-SIG-Finance] Forcasting VAR/VEC
>> To: "Megh Dal"<megh700004 at yahoo.com>, "R Finance"<r-sig-finance at stat.math.ethz.ch>, "Dr. Bernhard Pfaff"<bernhard at pfaffikus.de>
>> Date: Thursday, December 16, 2010, 2:47 PM
>>
>> library(vars)
>> data(Canada)
>> modeleVAR<-VAR(Canada, p=2)
>>
>> class(modeleVAR) # so you have your varest object!
>> predict(modeleVAR, n.ahead=3)
>>
>> Hope this helps?
>>
>> Matthieu
>>
>> Le 14. 12. 10 18:38, Megh Dal a écrit :
>>> I tied to post it through Nabble, however it perhaps
>> failed........ Here is my query. Your help will be highly
>> appreciated.
>>>
>>> I found a direct problem while implementing predict
>> method. To use that, I need to give values for argument
>> "object":
>>>
>>> object: An object of class ‘‘varest’’;
>> generated by ‘VAR()’, or an
>>> object of
>> class ‘‘vec2var’’; generated by ‘vec2var()’.
>>>
>>> Here what I have I the estimated coefficients and last
>> few values (required for prediction). Given those, how can I
>> construct object of that class?
>>>
>>> For example, I have VAR[2] model with estimated
>> coefficients:
>>>
>>> A1<- t(matrix(c(-0.9444135, 0.5289205,
>> 1.54338344, -1.2059849, 0.08658383, -0.7626375,
>> 0.5384060, 1.34211012, -0.1540462, 0.07684465,
>>> -1.5000912, 0.5975748, 2.08629175,
>> -1.3629879, 0.16405863, 0.5622497, -0.5749668,
>> -0.01207330, 1.6547463, -0.37280903, -1.1190102,
>>> 0.4834678, 1.04152221, -0.5084529,
>> 0.91748918), 5))
>>>
>>> A2<- t(matrix(c(-0.5281084, 0.6750694,
>> -0.00700632, 0.5744008, -0.540538174,
>> -0.5115759, 0.6663074, -0.03690401, 0.1015040,
>>> -0.483984397, -0.6669159, 0.7810531,
>> -0.08478713, 0.2837110, -0.634807434, 0.2474859,
>> -0.2126529, -0.04611398, -0.2851814, -0.003198122,
>>> 0.1136257, -0.4070806, -0.15362813, 0.1043871,
>> -0.570401589), 5))
>>>
>>> ## and deterministic terms with monthly seasonal dummy
>> (11 dummy variables + constant):
>>>
>>> Mu<- t(matrix(c(0.01971314683, 7.392074e-05,
>> 0.06745512042, 0.03066764063, -0.05716243051,
>> -0.01666261294, -0.02648375478, -0.07739940822,
>>> -0.08537205872, -0.03454705454, 0.01026347102,
>> 3.93408495893, 0.01124435027, 0.00826573416, 0.07232184812,
>> 0.02643898799, -0.02290620244,
>>> 0.00631679187, -0.02120713774, -0.06034222549,
>> -0.06737971493, -0.02466344307, 0.01574220616,
>> 3.67816873865, 0.01817359276, 0.00511780538,
>>> 0.06717006338, 0.06206760303, -0.04353428003,
>> -0.03033956305, -0.00535023233, -0.06992876937,
>> -0.10020345332, -0.04323120694, 0.00399287988,
>>> 3.01849213684,-0.00696500762, 0.00784220761,
>> 0.00337317617, -0.00771413903, 0.03016416594, 0.02404299199,
>> 0.00752992866, 0.01211247641,
>>> 0.01442465011, 0.00162618787, 0.00646325736,
>> 0.18377741575,0.06172376263, 0.03093119587, 0.00519357485,
>> -0.00720735363, -0.02268228948,
>>> -0.03428031321, 0.01599783772, -0.01470977952,
>> -0.00498472297, -0.03734476303, -0.03712904004,
>> 2.95905626288), ncol=5))
>>>
>>> ## and last 4 values of TS
>>>
>>> YY<- t(matrix(c(-0.55367226, 3.276980, -0.57634682,
>> 3.827854, 4.248918, -0.57806007, 3.254492, -0.61281320,
>> 3.835869, 4.280090, -0.51697066,
>>> 3.300236, -0.55756556, 3.815981, 4.274613,
>> -0.47619582, 3.320164, -0.48689008, 3.793915, 4.216955),
>> 5))
>>>
>>> Given those information, how I can predict values for
>> next 2 steps?
>>>
>>> Thanks,
>>>
>>> --- On Mon, 12/13/10, Pfaff, Bernhard Dr.<Bernhard_Pfaff at fra.invesco.com>
>> wrote:
>>>
>>>> From: Pfaff, Bernhard Dr.<Bernhard_Pfaff at fra.invesco.com>
>>>> Subject: AW: [R-SIG-Finance] Forcasting VAR/VEC
>>>> To: "Megh"<megh700004 at yahoo.com>,
>> r-sig-finance at stat.math.ethz.ch
>>>> Date: Monday, December 13, 2010, 2:19 PM
>>>> ?vars::predict
>>>>
>>>>> -----Ursprüngliche Nachricht-----
>>>>> Von: r-sig-finance-bounces at r-project.org
>>>>> [mailto:r-sig-finance-bounces at r-project.org]
>>>> Im Auftrag von Megh
>>>>> Gesendet: Montag, 13. Dezember 2010 07:57
>>>>> An: r-sig-finance at stat.math.ethz.ch
>>>>> Betreff: [R-SIG-Finance] Forcasting VAR/VEC
>>>>>
>>>>>
>>>>> Hi dears, I am looking for some function to
>> predict
>>>> through
>>>>> horizon "h", say, given the corefficients of a
>> VAR/VEC
>>>> model.
>>>>> I have moderately gone through packages
>> "urca"&
>>>> "vars" but
>>>>> did not find any significant:
>>>>>
>>>>>> ls("package:vars")
>>>>> [1] "A"
>>>> "Acoef"
>> "arch"
>>>>> "arch.test" "B"
>>>>
>>>>> "Bcoef"
>> "BQ"
>>>>
>> "causality"
>>>>> [9] "fanchart"
>>>> "fevd"
>> "irf"
>>>>
>>>>> "normality"
>> "normality.test" "Phi"
>>>> "Psi"
>> "restrict"
>>>>> [17] "roots"
>>>> "serial"
>> "serial.test"
>>>>> "stability" "SVAR"
>>>> "SVEC"
>> "VAR"
>> "VARselect"
>>>>> [25] "vec2var"
>>>>>> ls("package:urca")
>>>>> [1] "ablrtest"
>>>> "alphaols" "alrtest"
>> "bh5lrtest"
>>>>> "bh6lrtest" "blrtest"
>>>> "ca.jo"
>> "ca.po"
>>>>> [9] "cajolst"
>>>> "cajools"
>> "cajorls"
>> "lttest"
>>>>> "plot"
>>>>
>>>>> "plotres"
>>>> "punitroot"
>> "qunitroot"
>>>>> [17] "summary"
>>>> "unitrootTable"
>> "ur.df" "ur.ers"
>>>>
>>>>> "ur.kpss"
>> "ur.pp"
>>>>
>> "ur.sp"
>> "ur.za"
>>>>> Can somebofy help in finding any relevant
>> function for
>>>> prediction?
>>>>> Thanks,
>>>>> --
>>>>> View this message in context:
>>>>> http://r.789695.n4.nabble.com/Forcasting-VAR-VEC-tp3084994p308
>>>> 4994.html
>>>>> Sent from the Rmetrics mailing list archive
>> at
>>>> Nabble.com.
>>>>>
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--
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock
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