Hi Jon,
sorry for the inconvenience. I have done it in plain text now.
I am building a VAR model to forecast of bivariate time series. But it
shows flat forecast and i am in need of correcting it. Is there any way to
correct this flat forecast? or Do i have to go with other models?
Code:
> datax.zoo <- read.zoo(datax)
> datax.ts <- ts(datax.zoo)
> v1b <- VARselect(datax.ts, lag.max = 10, type = "const")
> v1b$selection
AIC(n) HQ(n) SC(n) FPE(n)
10 7 3 10
> var7 = VAR(datax.ts, p=7)
> serial.test(var7, lags.pt=10, type = "PT.asymptotic")
Portmanteau Test (asymptotic)
data: Residuals of VAR object var7
Chi-squared = 31.991, df = 12, p-value = 0.001388
> gf1 <- forecast(var7, h = 600)
> plot(gf1, main = "var7")
> grangertest(datax.ts[,1] ~ datax.ts[,2], order = 7)
Granger causality test
Model 1: datax.ts[, 1] ~ Lags(datax.ts[, 1], 1:7) + Lags(datax.ts[, 2], 1:7)
Model 2: datax.ts[, 1] ~ Lags(datax.ts[, 1], 1:7)
Res.Df Df F Pr(>F)
1 9968
2 9975 -7 20.852 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> grangertest(datax.ts[,2] ~ datax.ts[,1], order = 7)
Granger causality test
Model 1: datax.ts[, 2] ~ Lags(datax.ts[, 2], 1:7) + Lags(datax.ts[, 1], 1:7)
Model 2: datax.ts[, 2] ~ Lags(datax.ts[, 2], 1:7)
Res.Df Df F Pr(>F)
1 9968
2 9975 -7 3.0918 0.002948 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The P value from "Portmanteau Test" is very much less than << 0.05 for
lagged value 7. Is this correct?
I have added my plot and raw data in the attachment for your further
reference. Thank you.
Regards| Mit freundlichen Grüßen,
> Dhivya Narayanasamy
Regards| Mit freundlichen Grüßen,
Dhivya Narayanasamy
Contact No: +91-8438505020
On Sun, May 21, 2017 at 5:40 PM, John C Frain <fra...@gmail.com> wrote:
> It would be much easier to see what you are doing if you reposted in plain
> text.
>
> John C Frain
> 3 Aranleigh Park
> Rathfarnham
> Dublin 14
> Ireland
> www.tcd.ie/Economics/staff/frainj/home.html
> mailto:fra...@tcd.ie
> mailto:fra...@gmail.com
>
> On 21 May 2017 at 06:05, Dhivya Narayanasamy <dhiv.shr...@gmail.com>
> wrote:
>
>> I am building a VAR model to forecast of bivariate timeseries. But it
>> shows
>> flat forecast.
>>
>> So I would like to use recursive window forecasting technique using VAR
>> model. Will it give what i expect (Avoid flat forecast) ? or should i have
>> to go with other package.
>>
>> > datax.zoo <- read.zoo(datax)> datax.ts <- ts(datax.zoo)> v1b <-
>> VARselect(datax.ts, lag.max = 10, type = "const")> v1b
>> $selection
>> AIC(n) HQ(n) SC(n) FPE(n)
>> 9 7 5 9
>>
>> $criteria
>> 1 2 3 4
>> 5 6 7
>> AIC(n) 9.686513 9.657172 9.632444 9.625856
>> 9.621148 9.619425 9.615396
>> HQ(n) 9.688951 9.661234 9.638131 9.633167
>> 9.630085 9.629987 9.627583
>> SC(n) 9.693514 9.668839 9.648778 9.646856
>> 9.646815 9.649759 9.650397
>> FPE(n) 16099.014774 15633.507506 15251.665643 15151.510512
>> 15080.352425 15054.392389 14993.864861
>> 8 9 10
>> AIC(n) 9.615430 9.615116 9.615990
>> HQ(n) 9.629241 9.630552 9.633051
>> SC(n) 9.655098 9.659451 9.664991
>> FPE(n) 14994.366572 14989.661383 15002.762011
>> > var7 = VAR(datax.ts, p=7)> serial.test(var7, lags.pt=10, type =
>> "PT.asymptotic")
>>
>> Portmanteau Test (asymptotic)
>>
>> data: Residuals of VAR object var7Chi-squared = 22.745, df = 12,
>> p-value = 0.02997
>> > grangertest(datax.ts[,1] ~ datax.ts[,2], order = 7)Granger causality
>> test
>> Model 1: datax.ts[, 1] ~ Lags(datax.ts[, 1], 1:7) + Lags(datax.ts[,
>> 2], 1:7)Model 2: datax.ts[, 1] ~ Lags(datax.ts[, 1], 1:7)
>> Res.Df Df F Pr(>F) 1 5686 2
>> 5693 -7 16.105 < 2.2e-16 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01
>> ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1> grangertest(datax.ts[,2] ~ datax.ts[,1],
>> order = 7)Granger causality test
>> Model 1: datax.ts[, 2] ~ Lags(datax.ts[, 2], 1:7) + Lags(datax.ts[,
>> 1], 1:7)Model 2: datax.ts[, 2] ~ Lags(datax.ts[, 2], 1:7)
>> Res.Df Df F Pr(>F)1 5686 2 5693 -7 1.5618
>> 0.1418
>> > g <- forecast(var7, h = 600)> plot(g)
>>
>>
>> Also the 'P' value from portmanteau test shows auto correlation is
>> present
>> is my VAR model. Here is my raw data you can find :
>> https://drive.google.com/file/d/0B7I0DT-PiG4RenVkdXV3OFJLYVk
>> /view?usp=sharing
>>
>>
>> Thank you.
>>
>> Regards
>> > Dhivya Narayanasamy
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
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>> and provide commented, minimal, self-contained, reproducible code.
>
>
>
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