2017Q3GDP vol.2

getSymbols("GDP",src="FRED")
getSymbols("GDPC96",src="FRED")
getSymbols("PAYEMS",src="FRED")
getSymbols("UNDCONTSA",src="FRED")
beep(1)
PA <- PAYEMS
UC <- UNDCONTSA
kikan <- "1992-01-01::2017-09-30"
SP5 <- as.xts(read.zoo(read.csv("~/SP5.csv")))
G <- GDP
summary(lm(apply.quarterly(SP5[kikan],mean)[,1] ~ apply.quarterly(PA[kikan],mean) * apply.quarterly(UC[kikan],mean) * G[kikan] - apply.quarterly(UC[kikan],mean) ))
as.xts(forecast(auto.arima(UC),h=10)$mean[1:10],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=10))))[(3-month(index(last(UC))) %% 3) + c(1,4,7)]
as.xts(forecast(auto.arima(PA),h=10)$mean[1:10],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=10))))[(3-month(index(last(UC))) %% 3) + c(1,4,7)]
l <- 120  # # of months to predict
i <- seq(2,l/3,1)  # seq of quarters to predict
d <- as.Date(as.yearqtr(seq(Sys.Date(),as.Date("2100-12-31"),by="quarters")[i])) # pick up the first day of each quarters.
r <- 1.05 # pesumed GDP growth rate
last(PA);last(UC);last(GDP)
m_GDP <- as.xts(as.vector(last(GDP)) * r**(i/4),d)
m_PA <- (as.xts(forecast(auto.arima(PA),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=l))))[(3-month(index(last(PA))) %% 3) + seq(1,l-3,3)])[d]
m_UC <- (as.xts(forecast(auto.arima(UC),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=l))))[(3-month(index(last(UC))) %% 3) + seq(1,l-3,3)])[d]
my_sp5(kikan,m_GDP,m_PA,m_UC)
m_PA
beep(1)