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
Source
Bank of England Inflation Report November 2013. Retrieved from "Parameters for MPC CPI Inflation Projections from February 2004" spreadsheet. Original spreadsheet no longer available on the Bank of England website, but a copy exists at https://github.com/guyabel/fanplot/tree/master/data-raw/
Details
mode, uncertainty, and skew parameters relate to those
given in dsplitnorm, where uncertainty is the standard deviation.
Examples
## 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
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1.310928 0.8728139 0.6377539 0.1755382 -0.1673062 -0.3670966 -0.7036235
#> [2,] 1.726639 1.4725288 1.3942125 1.0410359 0.7458961 0.5665505 0.2436535
#> [3,] 1.948254 1.7922346 1.7974778 1.5024295 1.2327209 1.0642744 0.7486433
#> [4,] 2.097776 2.0079386 2.0695589 1.8137296 1.5611793 1.4000863 1.0893576
#> [5,] 2.216611 2.1793733 2.2858004 2.0611410 1.8222275 1.6669789 1.3601465
#> [6,] 2.318561 2.3264490 2.4713164 2.2733980 2.0461837 1.8959490 1.5924592
#> [7,] 2.410116 2.4585275 2.6379154 2.4640113 2.2473033 2.1015713 1.8010833
#> [8,] 2.494955 2.5809180 2.7922943 2.6406430 2.4336706 2.2921110 1.9944046
#> [9,] 2.575458 2.6970545 2.9387847 2.8082492 2.6105149 2.4729145 2.1778475
#> [10,] 2.653347 2.8094180 3.0805159 2.9704101 2.7816138 2.6478440 2.3553307
#> [11,] 2.730000 2.9200000 3.2200000 3.1300000 2.9500000 2.8200000 2.5300000
#> [12,] 2.806653 3.0305820 3.3594841 3.2895899 3.1183862 2.9921560 2.7046693
#> [13,] 2.884542 3.1429455 3.5012153 3.4517508 3.2894851 3.1670855 2.8821525
#> [14,] 2.965045 3.2590820 3.6477057 3.6193570 3.4663294 3.3478890 3.0655954
#> [15,] 3.049884 3.3814725 3.8020846 3.7959887 3.6526967 3.5384287 3.2589167
#> [16,] 3.141439 3.5135510 3.9686836 3.9866020 3.8538163 3.7440510 3.4675408
#> [17,] 3.243389 3.6606267 4.1541996 4.1988590 4.0777725 3.9730211 3.6998535
#> [18,] 3.362224 3.8320614 4.3704411 4.4462704 4.3388207 4.2399137 3.9706424
#> [19,] 3.511746 4.0477654 4.6425222 4.7575705 4.6672791 4.5757256 4.3113567
#> [20,] 3.733361 4.3674712 5.0457875 5.2189641 5.1541039 5.0734495 4.8163465
#> [21,] 4.149072 4.9671861 5.8022461 6.0844618 6.0673062 6.0070966 5.7636235
#> [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] -0.89341398 -1.1229949 -1.2362583 -1.3595218 -1.52604877 -1.57604877
#> [2,] 0.07430785 -0.1143834 -0.2208319 -0.3372804 -0.49017751 -0.54017751
#> [3,] 0.59019678 0.4233037 0.3204882 0.2076727 0.06204162 0.01204162
#> [4,] 0.93826459 0.7860786 0.6857142 0.5753499 0.43462125 0.38462125
#> [5,] 1.21489785 1.0744006 0.9759844 0.8675681 0.73073572 0.68073572
#> [6,] 1.45222455 1.3217552 1.2250103 1.1182654 0.98477558 0.93477558
#> [7,] 1.66535127 1.5438872 1.4486432 1.3433992 1.21291122 1.16291122
#> [8,] 1.86284494 1.7497257 1.6558725 1.5520193 1.42431289 1.37431289
#> [9,] 2.05024711 1.9450463 1.8525128 1.7499793 1.62491240 1.57491240
#> [10,] 2.23156089 2.1340212 2.0427646 1.9415080 1.81899475 1.76899475
#> [11,] 2.41000000 2.3200000 2.2300000 2.1300000 2.01000000 1.96000000
#> [12,] 2.58843911 2.5059788 2.4172354 2.3184920 2.20100525 2.15100525
#> [13,] 2.76975289 2.6949537 2.6074872 2.5100207 2.39508760 2.34508760
#> [14,] 2.95715506 2.8902743 2.8041275 2.7079807 2.59568711 2.54568711
#> [15,] 3.15464873 3.0961128 3.0113568 2.9166008 2.80708878 2.75708878
#> [16,] 3.36777545 3.3182448 3.2349897 3.1417346 3.03522442 2.98522442
#> [17,] 3.60510215 3.5655994 3.4840156 3.3924319 3.28926428 3.23926428
#> [18,] 3.88173541 3.8539214 3.7742858 3.6846501 3.58537875 3.53537875
#> [19,] 4.22980322 4.2166963 4.1395118 4.0523273 3.95795838 3.90795838
#> [20,] 4.74569215 4.7543834 4.6808319 4.5972804 4.51017751 4.46017751
#> [21,] 5.71341398 5.7629949 5.6962583 5.6195218 5.54604877 5.49604877
## Q4 2013 (coarser fan)
y0 <- 2013.75
boe0 <- subset(boe, time0 == y0)
k <- nrow(boe0)
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
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 1.196639 0.7825288 0.3642125 -0.008964106 -0.08410386 -0.2734495
#> [2,] 1.686611 1.4893733 1.2558004 1.011141033 0.99222755 0.8269789
#> [3,] 2.045458 2.0070545 1.9087847 1.758249179 1.78051488 1.6329145
#> [4,] 2.354542 2.4529455 2.4712153 2.401750821 2.45948512 2.3270855
#> [5,] 2.713389 2.9706267 3.1241996 3.148858967 3.24777245 3.1330211
#> [6,] 3.203361 3.6774712 4.0157875 4.168964106 4.32410386 4.2334495
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] -0.3463465 -0.4056922 -0.5043834 -0.5108319 -0.5272804 -0.5501775
#> [2,] 0.7701465 0.7348978 0.6844006 0.6859844 0.6775681 0.6707357
#> [3,] 1.5878475 1.5702471 1.5550463 1.5625128 1.5599793 1.5649124
#> [4,] 2.2921525 2.2897529 2.3049537 2.3174872 2.3200207 2.3350876
#> [5,] 3.1098535 3.1251022 3.1755994 3.1940156 3.2024319 3.2292643
#> [6,] 4.2263465 4.2656922 4.3643834 4.3908319 4.4072804 4.4501775
#> [,13]
#> [1,] -0.5501775
#> [2,] 0.6707357
#> [3,] 1.5649124
#> [4,] 2.3350876
#> [5,] 3.2292643
#> [6,] 4.4501775