《时间序列分析及应用.R语言》第十一章阅读笔记

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11.1干预分析

library(TSA)
win.graph(width = 4.875,height = 2.5,pointsize = 8)
data(airmiles)
plot(log(airmiles),ylab = 'log(airmiles',xlab = 'year')

#美国航空的每月客运里程：1996年1月~2005年5月

as.vector(diff(diff(window(log(airmiles),end = c(2001,8)),12))),lag.max = 48)

#干预期（1-B）（1-B12）log（航空客运里程）的样本ACF　　

air.ma1 <- arimax(log(airmiles),order = c(0,1,1),
                  seasonal = list(order = c(0,1,1),period = 12
                  ),xtransf = data.frame(I911 = 1*(seq(airmiles)==69),
                  I911 = 1*(seq(airmiles)==69)),
                  transfer = list(c(0,0),c(1,0)),xreg = data.frame(
                Dec96 = 1*(seq(airmiles)==12),
                  Jan97 = 1*(seq(airmiles)==13),Dec02 = 1*(seq(airmiles)==84)),
                  method = 'ML')
air.ma1

　　#对数化航空客运里程的干预模型的估计

plot(log(airmiles),ylab = 'Log(airmiles)')
points(fitted(air.ma1))

　　#对数化的航空客运里程与拟合值

Nine11p <- 1*(seq(airmiles)==69)
plot(ts(Nine11p*(-0.0949)+filter(Nine11p,filter = 0.8139,method = 'recursive',side = 1)
        *(-0.2715),frequency = 12,start = 1996),ylab = '9/11',type = 'h')
abline(h = 0)

　　#9.11事件对航空客运量序列所造成影响的估计