分解商业周期时间序列：线性滤波器、HP滤波器、Baxter滤波器、Beveridge Nelson分解等去趋势法|附代码数据

rm(list = ls())
graphics.off()

gdp <- ts(dat.tmp, start = c(1960, 2), frequency = 4)

plot(gdp)

lin.mod$fitted.values  # 拟合值与时间趋势有关
ts(lin.trend, start = c(1960, 1))  # 为趋势创建一个时间序列变量
gdp - linear  # 周期是数据和线性趋势之间的差异

plot.ts(gdp, ylab = "")  
lines(linear, col = "red")  
legend("topleft", legend = c("data", "trend")

hp(gdp, freq = 1600)
plot.ts(gdp, ylab = "")  # 绘制时间序列
plot.ts(hp.decom$cycle, ylab = "")  # 绘制周期图

bk(gdp, pl = 6, pu = 32)
plot.ts(gdp, ylab = "")
plot.ts(cycle, ylab = "")

plot.ts(gdp, ylab = "")

plot.ts(cfcycle, ylab = "")

plot.ts(gdp, ylab = "")
lines(bn.trend, col = "red")
plot.ts(bn.cycle, ylab = "")

 ts.union(lin.cycle, hp.decom, bp.decom, 
    cf.decom, bn.cycle)

plot.ts(comb, ylab = "")

library(tsm)
library(TSA)
library(mFilter)

2 * cos(2 * pi * t * w[1]) + 3 * sin(2 * pi * t * 
    w[1])  # no.obs点上的6个周期的频率
4 * cos(2 * pi * t * w[2]) + 5 * sin(2 * pi * t * 
    w[2])  #频率为10个周期的观察点
6 * cos(2 * pi * t * w[3]) + 7 * sin(2 * pi * t * 
    w[3])  # 在没有观测点的情况下，频率为40个周期
y <- x1 + x2 + x3

par(mfrow = c(2, 2), mar = c(2.2, 2.2, 2, 1), cex = 0.8)
plot(x1, type = "l", main = "x1")
plot(x2, type = "l", main = "x2")
plot(x3, type = "l", main = "x3")
plot(y, type = "l", main = "y")

gram(y, main = "y", col = "red")

cf(y0)
gram(cycle)

plot(x1, type = "l", lty = 1)
lines(cycle, lty = 3, lwd = 3)
plot(y, type = "l", lty = 1)
lines(cycle, lty = 3, lwd = 3)

diff/cpi[-1 * (length - 11):length]

plot(inf.yoy)

wt(yoy, "d4")

plot.ts(yoy)
for (i in 1:4) plot.ts(d4[[i]]

plot.ts(inf, ylab = "inf")
lines(ren)

inf.tmp <- inf.tren + inf.d4$w3
inf.tren2 <- ts(inf.tmp, start = c(2003, 1), frequency = 12)

plot.ts(inf.yoy, ylab = "inf")
lines(inf.tren2, col = "red")

yd <- dat[5:n.obs, ] - dat[1:(n.obs - 4), ]  # 存储输出

yc_li <- matrix(rep(0, n.obs * n.var), ncol = n.var)
yc_hp <- matrix(rep(0, n.obs * n.var), ncol = n.var)
yc_bp <- matrix(rep(0, n.obs * n.var), ncol = n.var)
yc_bn <- matrix(rep(0, n.obs * n.var), ncol = n.var)

for (i in 1:n) {
    
    # 用线性滤波器对数据进行去趋势处理
    lin.mod <- lm(dat[, i] ~ time(dat[, i]))
    
    # 用HP滤波器去趋势数据
    yc_hp[, i] <- hp.cycle
    
    #用带通滤波器去趋势数据
    yc_bp[, i] <- bp.cycle
    
    #  Beveridge-Nelson分解
    yc_bn[, i] <- bn.[, 2]
}

for (i in 1:n) {
    for (j in 1:n.var) {
        c_li <- leadlag(yc_li[, i], yc_li[, j], maxLeadLag)
        c_hp <- leadlag(yc_hp[, i], yc_hp[, j], maxLeadLag)
        c_bp 
        c_bn
        c_yd 

        
        for (k in 1:5) {

            ynamesLong[(cnt + k), 1] <- paste(ynames.tmp)
        }
        cnt <- cnt + 5

# 线性趋势

barplot(corrStylizedFact)
box()

# hp滤波器

op <- par(mfrow = c(1, 3))
barplot(corrStyli, ylim = c(-1, 1))
box()

# beveridge nelson 分解
barplot(coracts, ylim = c(-1, 1), col = "red")
box()