[R-SIG-Finance] Forcasting VAR/VEC
mat
matthieu.stigler at gmail.com
Thu Dec 16 10:17:06 CET 2010
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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>
>
>
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