fanplot (version 4.0.0)

boe: Parameters for MPC CPI Inflation Projections from Q1 2004 to Q4 2013.

Description

Numerical parameters for inflation report of the Bank of England used to specify the probability distributions for forecast charts of CPI inflation. Data formatted from the November 2013 Bank of England Inflation Report.

Usage

data(boe)

Format

A data frame with 512 observations on the following 5 variables.

time0

Publication time of parameters

time

Future time of projected parameter

mode

Central location parameter of split-normal distribution

uncertainty

Uncertainty parameter of split-normal distribution

skew

Skew parameter of split-normal distribution

Details

mode, uncertainty and skew parameters relate to those given in dsplitnorm, where uncertainty is the standard deviation.

Examples

# NOT RUN {
##
##Q1 2013
##
#extract data for Q1 2013
y0 <- 2013
boe0<-subset(boe, time0==y0)
k <- nrow(boe0)

#guess work to set percentiles the boe are plotting
p <- seq(0.05, 0.95, 0.05)
p <- c(0.01, p, 0.99)

#estimate percentiles for future time period
pp <- matrix(NA, nrow = length(p), ncol = k)
for (i in 1:k)
  pp[, i] <- qsplitnorm(p, mode = boe0$mode[i], sd = boe0$uncertainty[i], skew = boe0$skew[i])
pp

#plot cpi
par(mar=rep(2,4))
plot(cpi, type = "l", xlim = floor(c(y0-5, y0+3)), ylim = c(-2, 7), las = 1, 
 col="tomato", lwd=2, xaxt = "n", yaxt = "n")

#backround
rect(y0-0.25, par("usr")[3] - 1, y0+3, par("usr")[4] + 1, border = "gray90", col = "gray90")

#fan
pal <- colorRampPalette(c("tomato", "gray90"))
fan(data=pp, probs=p, sim.data=FALSE, start=y0, frequency=4, 
 anchor=cpi[time(cpi)==y0-0.25], fan.col=pal, ln=NULL, rlab=FALSE)

#aesthetics for boe axis
axis(2, at = -2:7, las = 2, tcl = 0.5, labels = FALSE)
axis(4, at = -2:7, las = 2, tcl = 0.5)
axis(1, at = 2008:2016, tcl = 0.5)
axis(1, at = seq(2008, 2016, 0.25), labels = FALSE, tcl = 0.2)
abline(h = 2) #cpi target
abline(v = y0 + 1.75, lty = 2) #2 year line


##
##Q4 2013 (coarser fan)
##
#extract data for Q4 2013
y0 <- 2013.75
boe0<-subset(boe, time0==y0)
k <- nrow(boe0)

#guess work at which percentiles the boe are plotting
p <- seq(0.2, 0.8, 0.2)
p <- c(0.05, p, 0.95)
pp <- matrix(NA, nrow = length(p), ncol = k)
for (i in 1:k)
  pp[, i] <- qsplitnorm(p, mode = boe0$mode[i], sd = boe0$uncertainty[i], skew = boe0$skew[i])
pp

#define prediction intervals for labels
p.int<-p[4:6]-p[3:1]
p.int

#plot cpi
par(mar=rep(2,4))
plot(cpi, type = "l", xlim = c(y0-5, y0+3), ylim = c(-2, 7), las = 1, 
 col="tomato", lwd=2, xaxt = "n", yaxt = "n")

#backround
rect(y0-0.25, par("usr")[3] - 1, y0+3, par("usr")[4] + 1, border = "gray90", col = "gray90")

# add fan
pal <- colorRampPalette(c("tomato", "gray90"))
fan(data=pp, probs=p.int, sim.data=FALSE, start=y0, frequency=4,
 anchor=cpi[time(cpi)==y0-0.25], fan.col=pal, ln=NULL, rlab=pi, nfan=4, type="interval")

#aesthetics for boe axis
axis(2, at = -2:7, las = 2, tcl = 0.5, labels = FALSE)
axis(4, at = -2:7, las = 2, tcl = 0.5)
axis(1, at = 2008:2016, tcl = 0.5)
axis(1, at = seq(2008, 2016, 0.25), labels = FALSE, tcl = 0.2)
abline(h = 2) #cpi target
abline(v = y0 + 1.75, lty = 2) #2 year line
# }