# Exhibit 4.2 win.graph(width=4.875, height=3,pointsize=8) data(ma1.2.s) plot(ma1.2.s,ylab=expression(Y[t]),type='o') # An MA(1) series with MA coefficient equal to -0.9 and # of length n=100 can be simulated by the following command set.seed(12345) # initialize the seed of the random number generator so that # the simulations can be reproduced. y=arima.sim(model=list(ma=-c(-0.9)),n=100) # Note that R uses the plus convention in the model formula so the # additional minus sign. # Exhibit 4.3 win.graph(width=3, height=3,pointsize=8) plot(y=ma1.2.s,x=zlag(ma1.2.s),ylab=expression(Y[t]),xlab=expression(Y[t-1]),type='p') # Exhibit 4.4 plot(y=ma1.2.s,x=zlag(ma1.2.s,2),ylab=expression(Y[t]),xlab=expression(Y[t-2]),type='p') # Exhibit 4.5 win.graph(width=4.875, height=3,pointsize=8) data(ma1.1.s) plot(ma1.1.s,ylab=expression(Y[t]),type='o') # An MA(1) series with ma coefficient equal to 0.9 and # of length n=100 can be simulated by the following command y=arima.sim(model=list(MA=-c(0.9)),n=100) # Note that R uses the plus convention in the MA model formula so the # additional minus sign. # Exhibit 4.6 win.graph(width=3, height=3,pointsize=8) plot(y=ma1.1.s,x=zlag(ma1.1.s),ylab=expression(Y[t]),xlab=expression(Y[t-1]),type='p') # Exhibit 4.7 plot(y=ma1.1.s,x=zlag(ma1.1.s,2),ylab=expression(Y[t]),xlab=expression(Y[t-2]),type='p') # Exhibit 4.8 win.graph(width=4.875, height=3,pointsize=8) data(ma2.s) plot(ma2.s,ylab=expression(Y[t]),type='o') # An MA(2) series with MA coefficients equal to 1 and -0.6 and # of length n=100 can be simulated by the following command y=arima.sim(model=list(ma=-c(1, -0.6)),n=100) # Note that R uses the plus convention in the MA model formula so the # additional minus sign. # Exhibit 4.9 win.graph(width=3, height=3,pointsize=8) plot(y=ma2.s,x=zlag(ma2.s),ylab=expression(Y[t]),xlab=expression(Y[t-1]),type='p') # Exhibit 4.10 plot(y=ma2.s,x=zlag(ma2.s,2),ylab=expression(Y[t]),xlab=expression(Y[t-2]),type='p') # Exhibit 4.11 plot(y=ma2.s,x=zlag(ma2.s,3),ylab=expression(Y[t]),xlab=expression(Y[t-3]),type='p') # Exhibit 4.13 win.graph(width=4.875, height=3,pointsize=8) data(ar1.s) plot(ar1.s,ylab=expression(Y[t]),type='o') # An AR(1) series with AR coefficient equal to 0.9 and # of length n=100 can be simulated by the following command y=arima.sim(model=list(ar=c(0.9)),n=100) # Note that the R convention for the AR model formula is same as the book, so # NO additional minus sign. # Exhibit 4.14 win.graph(width=3, height=3,pointsize=8) plot(y=ar1.s,x=zlag(ar1.s),ylab=expression(Y[t]),xlab=expression(Y[t-1]),type='p') # Exhibit 4.15 plot(y=ar1.s,x=zlag(ar1.s,2),ylab=expression(Y[t]),xlab=expression(Y[t-2]),type='p') # Exhibit 4.16 plot(y=ar1.s,x=zlag(ar1.s,3),ylab=expression(Y[t]),xlab=expression(Y[t-3]),type='p') # Exhibit 4.19 win.graph(width=4.875, height=3,pointsize=8) data(ar2.s) plot(ar2.s,ylab=expression(Y[t]),type='o') # An AR(2) series with AR coefficients equal to 1.5 and -0.75 and # of length n=100 can be simulated by the following command y=arima.sim(model=list(ar=c(1.5,-0.75)),n=100) # Note that the R convention for the AR model formula is same as the book, so # NO additional minus sign.