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]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.