# Figures to illustrate the difference between forecasting in Trend 
        # Stationary and Unit Root Models for Lecture 9 of 508 
        u <- rnorm(120) 
        s <- 1:120 
        y <- .3*s+5*filter(u,c(.95,-.1),"recursive",init=rnorm(2)) 
        fit0 <- arima0(y,order=c(2,0,0),xreg=s) 
        fit1 <- arima0(y,order=c(2,1,0),xreg=s,include.mean=T) 
        fore0 <- predict(fit0,n.ahead=44,newxreg=121:164) 
        fore1 <- predict(fit1,n.ahead=44,newxreg=121:164) 
        pdf("fig1R.pdf",width=6.0,height=4) 
        par(mfrow=c(1,2)) 
        ts.plot(y,fore0$pred,fore0$pred+2*fore0$se, fore0$pred-2*fore0$se, 
        gpars=list(lty=c( 1,2,3,3))) 
        abline(fit0$coef[3:4],lty=2) 
        ts.plot(y,fore1$pred,fore1$pred+2*fore1$se, fore1$pred-2*fore1$se, 
        gpars=list(lty=c( 1,2,3,3))) 
        abline(c(0,fit1$coef[3]),lty=2)
        dev.off()