Data handling
# Start Inf data
# --------------
# read data
dtf <- read_dta("Data/Brazil/startinf_brazil_m.dta", encoding = "latin1")
# labels of each variable
dtf_labels <- unlist(lapply(dtf, attr, which = "label"))
# remove labels
dtf <- as.data.frame(lapply(dtf, c))
# Notice that for a given frequency,
# the data begins at the start of the period
# it reports to (for example, monthly data's date is on the 1st of
# each month)
# fix date
datelookup <- seq.Date(from = as.Date("2005-01-01"),
to = as.Date("2021-12-01"),
by = "month")
dtf$date <- datelookup[dtf$date - abs(min(dtf$date)) + 1]
# order by date
dtf <- arrange(dtf, date)
# correct magnitude of certain variables
dtf <- dtf %>% mutate_at(vars(-date, -contains("_skew")),
~ ./100 )
# create max-min
dtf$IPCA12_max_minus_min <- dtf$IPCA12_max - dtf$IPCA12_min
# Create breakeven rates
# ----------------------
for (n0 in 1:(10-1)) {
var0 <- paste0("breakeven", n0)
for (nf in (n0+1):10) {
varf <- paste0("breakeven", nf)
var1 <- paste(sep = "_","breakeven_f", n0, nf)
# forward rate
dtf[,var1] <- ( (1 + dtf[,varf])^nf ) / ( (1 + dtf[,var0])^n0 )
dtf[,var1] <- exp( (1/(nf-n0)) * log(dtf[,var1])) - 1
# if desired, forwards are lagged so that
# their value at time t corresponds to
# the expectation of the tn-t0 rate at t-tn
if (bAdjustPeriodTiming) dtf[, var1] <- lag(dtf[, var1], nf-1)
}
}
# adjustment of lead data
# -----------------------
# be careful. If you use more than one year horizons, you have
# to adjust the lagging
if (bAdjustPeriodTiming) {
dtf <- dtf %>%
mutate_at(vars(starts_with("IPCA12_"), fgv_ibre_exp, matches("breakeven(\\d+)") # no forwards
), ~ lag(.,12-1) # monthly date
) %>%
# -1 because data has date 01mmyyyy, refers to previous period
mutate_at(vars(starts_with("fgv_md_")),
~ lag(.,12) #monthly date
) %>%
mutate(EIPCA_1y_median = lag(EIPCA_1y_median, 12),
EIPCA_2y_median = lag(EIPCA_2y_median, 12*2),
EIPCA_3y_median = lag(EIPCA_3y_median, 12*3)
)
# no "-1" because this data is end of month
}
# lag as it is realized inflation that one needs
dtf <- dtf %>% mutate_at(vars(IGPM, core_IPC, IPCA),
~ lag(., 12-1))
# notice the -1 since every data point is plotted at the start of the
# respective period (monthly freq <=> beginning of month)
# pivot data to long format for plotting
dtf_long <- pivot_longer(dtf, !date,
names_to = "variable",
values_to = "value",
values_transform = as.numeric)
Timespan is 2008/01 - 2016/12
Figure(s) 11
Subfigure 11(a): Actual inflation and its target
# actual and expected inflation
target <- 0.045
target_ub <- 0.065
# vertical line width
vline_w <- 0.6 #mm
legs <- c("Consumer Price Inflation",
"Consumer price inflation - admin prices",
"Consumer price inflation - free prices",
"Inflation Target",
"Upper Bound")
legv <- c("red", "blue", "orange3", "green4", "black")
linev <- c("solid", "dotted", "dotdash", "longdash", "solid")
labs <- c(0, 0.025, 0.045, 0.065, seq(0.1, 0.18, 0.04))
plot <- create_plot(dtf_long,
vars = c("IPCA", "IPCA_admin", "IPCA_free"),
scales = 0) +
geom_hline(aes(yintercept = target_ub, colour = "Upper Bound", linetype = "Upper Bound"),
size = tfont(vline_w)) +
geom_hline(aes(yintercept = target, colour = "Target", linetype = "Target"),
size = tfont(vline_w)) +
scale_linetype_manual(values = linev, labels = legs) +
scale_colour_manual(values = legv, labels = legs) +
scale_y_continuous(breaks = labs, labels = scales::percent(labs)) +
scale_y_continuous(limits = c(0, 0.20), breaks = labs, labels = scales::percent(labs))
Scale for ‘y’ is already present. Adding another scale for ‘y’, which will replace the existing scale. Scale for ‘y’ is already present. Adding another scale for ‘y’, which will replace the existing scale.
print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_11_a.pdf"), plot)
# key indicators for paper
print(mean(dtf$IPCA[dtf$date >= as.Date("2012-01-01") &
dtf$date <= as.Date("2016-12-31")], na.rm = TRUE
)
)
[1] 0.07144252
Subigure 11(c): Cross-sectional disagreement of households
# now using max-min instead of max and min
vars1 <- c("fgv_md_skew_100")
vars2 <- c("fgv_md_sd_100")
vars <- c(vars1, vars2)
legss <- c("Cross-sectional survey skewness (lhs)",
"Cross-sectional survey standard deviation (rhs)")
legv <- c("red", "blue")
linev <- c("solid", 'dotdash')
transf_b <- 90 # for secondary axis
transf_a <- 0
plot <- create_plot(dtf_long,
vars = vars1,
perc = 0, scales = 0) +
theme(legend.position = c(0.3,0.88)) +
geom_line(data = dtf_long %>%
filter(date >= as.Date("2008-01-01") &
date <= as.Date("2017-12-31") &
variable %in% vars2 & !is.na(value)),
aes(x = date, y = (value * transf_b) - transf_a,
colour = variable, linetype = variable),
size = line_sizee) +
scale_y_continuous(limits = c(0, 7.5),
sec.axis = sec_axis( trans = ~ (. + transf_a) / transf_b,
labels = scales::percent_format(accuracy = 0.1))
) +
scale_linetype_manual(breaks = vars, values = linev, labels = legss) +
scale_colour_manual(breaks = vars, values = legv, labels = legss)
print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_11_c.pdf"), plot)
---
title: "Brookings Paper - Brazil - Notebook"
output: html_notebook
---

```{r start_R, include = FALSE}

#----------------------------------------------------------------
# Initiation
#----------------------------------------------------------------

rm(list = ls())

# clean console
cat("\014")

#### PLEASE CHOOSE #####

# print and save plots?
bSavePlots <- 1   # 0: no
                  # 1: yes

# Expectations at T = T-h or T+h?
# will shift by 12 periods (monthly), be careful for e.g. quarterly data
bAdjustPeriodTiming <- 0   # 0: T+h
                           # 1: T-h
####  CHOICE END   #####

# BEWARE ONLY VARIABLES USED ARE ADJUSTED
# IF YOU WANT TO USE OTHER VARIABLES
# PLEASE ADD THEM BELOW WHEN THE ADJUSTMENT
# IS MADE

# packages
# list of libraries
packages <- c("tidyverse"   # data analysis
              ,"knitr"      # markdown
              ,"haven"      # read .dta files
              ,"readxl"     # read excel files
              ,"lubridate"  # dates
              ,"tinytex"    # for markdown
              ,"ks"         # kernel estimation
              ,"magrittr"   # pie operator
)

# install them if needed
new_packages <- packages[!(packages %in% installed.packages()[, "Package"])] 

if (length(new_packages) > 0){ #installs them
   install.packages(new_packages, dependencies = TRUE)
}

# load libraries
lapply(packages, require, character.only = TRUE)

```

```{r plot_def, include = FALSE}

# ggplot2 options

# size of figures for saving and displaying (disp will be adjusted)
# figure size in cm (for inches divide by 0.393700787)
fig_h <- 2.5 * 8.4375* 0.393700787 # width
fig_w <- 2.5 * 15 * 0.393700787    # height
fig_disp_adj <- 1                  # adjustment for figure display


# Convert from mm to internal units used in grid ggplot
tfont <- function(x) return(.pt*x)        # this way no res problems
tlwd  <- function(x) return(.stroke*x) 

# ggplot2 options
# plot definitions 

legend_text_size <- 7.5
axis_text_size   <- 7

legend_key_width <- 20 # mm

# size of line for plots
line_sizee <- tlwd(1)

theme_set(
  theme_minimal() +
            theme(panel.background = element_blank(),
                  plot.title = element_text(face = "plain", size = tfont(6)),
                  plot.subtitle = element_text(face = "italic"),
                  panel.grid.minor.x = element_blank(),
                  axis.ticks = element_blank(),
                  axis.line = element_line(),
                  legend.background = element_rect(colour="black",
                                                   fill = NA, size = 0.5),
                  legend.box.background = element_blank(),
                  legend.title = element_blank(),
                  legend.position = c(0.25,0.8),
                  legend.key = element_rect(colour = NA,
                                            fill = NA),
                  legend.key.width = unit(legend_key_width, "mm"),
                  legend.text = element_text(size = tfont(legend_text_size)),
                  axis.text  = element_text(size = tfont(axis_text_size)),
                  axis.title = element_blank()
                 )
)

# functions to create colour/line scheme

create_legv <- function(x) {

  #colours to be used in plots
  y <- c('red','blue','green4','orange3',"purple2")
  n <- length(x)
  y <- y[1:n]
  names(y) <- x 
  return(y)

}

create_linev <- function(x) {

  # linetypes to be used in plots
  y <- c('solid','dotted','dashed',"dotdash",'longdash')
  n <- length(x)
  y <- y[1:n]
  names(y) <- x
  return(y)

}

create_plot <- function(df,
                        vars,
                        legs = NULL,
                        date_lb = as.Date("2008-01-01"),
                        date_ub = as.Date("2017-12-31"),
                        perc = 1,
                        scales = 1,
                        line_size = line_sizee) {
  
  # date bounds
  date_lb <- as.Date(date_lb)
  date_ub <- as.Date(date_ub)
  
  # colours/linetypes
  legv <- create_legv(vars)
  linev <- create_linev(vars)
  
  df <- df %>%
        filter(date >= date_lb & date <= date_ub &
               variable %in% vars & !is.na(value)) %>%
        mutate(variable = factor(variable, levels = vars))
  
  plot <- ggplot(df, aes(x = date, y = value)) +
    geom_line(aes(colour = variable, linetype = variable),
              size = line_size) +
    scale_x_date(date_breaks = "1 year", date_labels = "%Y")
  
  if (scales) plot <- plot + 
        scale_linetype_manual(breaks = vars, values= linev, labels = legs) +
        scale_colour_manual(breaks = vars, values = legv, labels = legs) 
  
  if (perc) plot <- plot + 
        scale_y_continuous(labels = scales::percent_format(accuracy = 1))
  
  return(plot)
  
}

my_ggsave <- function(.filename, .plot,
                      .device = "pdf", .width = fig_w,
                      .height = fig_h, .units = "in", .dpi = 300, ...) {
  
  ggsave(filename = .filename, plot = .plot,
         device = .device,
         width = .width, height = .height,
         units = .units, dpi = .dpi, ...)
  
}

```

```{r setup, include = FALSE}

# directory
directory <- getwd()
directory <- gsub("/Code", "", directory)
opts_knit$set(root.dir = directory)

path_figure_out <- "./Figures/"

# default chunk options
opts_chunk$set(message = FALSE, warning = FALSE, results = 'asis', echo = TRUE,
               fig.width = fig_disp_adj * fig_w, fig.height = fig_disp_adj * fig_h,
               fig.retina = 1) # figure options

```

## Data handling

```{r load_data}

# Start Inf data
# --------------

# read data
dtf <- read_dta("Data/Brazil/startinf_brazil_m.dta", encoding = "latin1")

# labels of each variable
dtf_labels <- unlist(lapply(dtf, attr, which = "label"))

# remove labels
dtf <- as.data.frame(lapply(dtf, c))

# Notice that for a given frequency,
# the data begins at the start of the period
# it reports to (for example, monthly data's date is on the 1st of
# each month)

# fix date
datelookup <- seq.Date(from = as.Date("2005-01-01"),
                       to = as.Date("2021-12-01"),
                       by = "month")  

dtf$date <- datelookup[dtf$date - abs(min(dtf$date)) + 1]

# order by date
dtf <- arrange(dtf, date)

# correct magnitude of certain variables
dtf <- dtf %>% mutate_at(vars(-date, -contains("_skew")),
                         ~ ./100 )

# create max-min
dtf$IPCA12_max_minus_min <- dtf$IPCA12_max - dtf$IPCA12_min

# Create breakeven rates
# ----------------------

for (n0 in 1:(10-1)) {
  
  var0 <- paste0("breakeven", n0)
  
  for (nf in (n0+1):10) {
    
    varf <- paste0("breakeven", nf)
    var1 <- paste(sep = "_","breakeven_f", n0, nf)
    
    # forward rate
    dtf[,var1] <- ( (1 + dtf[,varf])^nf ) / ( (1 + dtf[,var0])^n0 )
    dtf[,var1] <- exp( (1/(nf-n0)) * log(dtf[,var1])) - 1
    
    # if desired, forwards are lagged so that
    # their value at time t corresponds to
    # the expectation of the tn-t0 rate at t-tn
    if (bAdjustPeriodTiming) dtf[, var1] <- lag(dtf[, var1], nf-1)
    
  } 
  
}

# adjustment of lead data
# -----------------------
# be careful. If you use more than one year horizons, you have
# to adjust the lagging

if (bAdjustPeriodTiming) {
  
  dtf <- dtf %>%
    mutate_at(vars(starts_with("IPCA12_"), fgv_ibre_exp, matches("breakeven(\\d+)") # no forwards
                  ), ~ lag(.,12-1) # monthly date
              ) %>%
    # -1 because data has date 01mmyyyy, refers to previous period
    mutate_at(vars(starts_with("fgv_md_")),
              ~ lag(.,12) #monthly date
              ) %>%
    mutate(EIPCA_1y_median = lag(EIPCA_1y_median, 12),
           EIPCA_2y_median = lag(EIPCA_2y_median, 12*2),
           EIPCA_3y_median = lag(EIPCA_3y_median, 12*3)
          )
    # no "-1" because this data is end of month
}

# lag as it is realized inflation that one needs
dtf <- dtf %>% mutate_at(vars(IGPM, core_IPC, IPCA),
                         ~ lag(., 12-1))
# notice the -1 since every data point is plotted at the start of the
# respective period (monthly freq <=> beginning of month)


# pivot data to long format for plotting
dtf_long <- pivot_longer(dtf, !date,
                         names_to = "variable",
                         values_to = "value",
                         values_transform = as.numeric)

```

## Timespan is 2008/01 - 2016/12

## Figure(s) 11
## Subfigure 11(a): Actual inflation and its target

```{r fig_11_a}

# actual and expected inflation 

target    <- 0.045
target_ub <- 0.065

# vertical line width
vline_w <- 0.6 #mm

legs <- c("Consumer Price Inflation",
          "Consumer price inflation - admin prices",
          "Consumer price inflation - free prices",
          "Inflation Target",
          "Upper Bound")
legv <- c("red", "blue", "orange3", "green4", "black")
linev <- c("solid", "dotted", "dotdash", "longdash", "solid")
labs <- c(0, 0.025, 0.045, 0.065, seq(0.1, 0.18, 0.04))

plot <- create_plot(dtf_long,
                    vars = c("IPCA", "IPCA_admin", "IPCA_free"),
                    scales = 0) +
  geom_hline(aes(yintercept = target_ub, colour = "Upper Bound", linetype = "Upper Bound"),
             size = tfont(vline_w)) +
  geom_hline(aes(yintercept = target, colour = "Target", linetype = "Target"),
             size = tfont(vline_w)) +
  scale_linetype_manual(values = linev, labels = legs) +
  scale_colour_manual(values = legv, labels = legs) +
  scale_y_continuous(breaks = labs, labels = scales::percent(labs)) +
  scale_y_continuous(limits = c(0, 0.20), breaks = labs, labels = scales::percent(labs))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_11_a.pdf"), plot)

# key indicators for paper
print(mean(dtf$IPCA[dtf$date >= as.Date("2012-01-01") &
                    dtf$date <= as.Date("2016-12-31")], na.rm = TRUE
          )
     )

```

## Subfigure 11(b): Markets and survey first-order moments

```{r fig_11_b}

# vertical line width
vline_w <- 0.6 #mm

vars <- c("breakeven1",
          "IPCA12_median",
          "fgv_ibre_exp")

breakss <- c(vars, "Target", "Upper Bound")
legs <- c("Market-price implied",
          "Survey of Professionals",
          "Survey of Households",
          "Inflation Target",
          "Upper Bound")

legv <- c("red", "blue", "orange3", "green4", "black")
linev <- c("solid", "dotted", "dotdash", "longdash", "solid")

plot <- create_plot(dtf_long,
                    vars = vars,
                    scales = 0) +
  geom_hline(aes(yintercept = target, colour = "Target", linetype = "Target"),
             size = tfont(vline_w)) +
  geom_hline(aes(yintercept = target_ub, colour = "Upper Bound", linetype = "Upper Bound"),
             size = tfont(vline_w)) +
  scale_linetype_manual(breaks = breakss, values= linev, labels = legs) +
  scale_colour_manual(breaks = breakss, values = legv, labels = legs) +
  scale_y_continuous(limits = c(0, 0.12), breaks = labs, labels = scales::percent(labs))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_11_b.pdf"), plot)

```

## Subigure 11(c): Cross-sectional disagreement of households

```{r fig_11_c}

# now using max-min instead of max and min

vars1 <- c("fgv_md_skew_100")
vars2 <- c("fgv_md_sd_100")
vars <- c(vars1, vars2)

legss <- c("Cross-sectional survey skewness (lhs)",
           "Cross-sectional survey standard deviation (rhs)")
legv <- c("red", "blue")
linev <- c("solid", 'dotdash')

transf_b <- 90 # for secondary axis
transf_a <- 0
  
plot <- create_plot(dtf_long,
                    vars = vars1,
                    perc = 0, scales = 0) +
  theme(legend.position = c(0.3,0.88)) +
  geom_line(data = dtf_long %>%
              filter(date >= as.Date("2008-01-01") &
                     date <= as.Date("2017-12-31") &
                     variable %in% vars2 & !is.na(value)),
            aes(x = date, y = (value * transf_b) - transf_a,
                colour = variable, linetype = variable),
            size = line_sizee) +
  scale_y_continuous(limits = c(0, 7.5),
                     sec.axis = sec_axis( trans = ~ (. + transf_a) / transf_b,
                                          labels = scales::percent_format(accuracy = 0.1))
                                        ) +
  scale_linetype_manual(breaks = vars, values = linev, labels = legss) +
  scale_colour_manual(breaks  = vars, values = legv, labels = legss)
  
print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_11_c.pdf"), plot)

```

## Subfigure 11(d): Cross-sectional distribution of household

```{r fig_11_d}

# read data
dtf_micro <- read.csv("Data/Brazil/fgv_household_density.csv", encoding = "latin1")
dtf_micro %<>% mutate(year = as.factor(year))

# line
linev <- c('dotdash', 'dotted', 'solid')
legv <- c('red', 'blue', 'green4')

plot <- ggplot(dtf_micro, aes(x = fgv_md_EIPCA)) +
  geom_line(aes(x = x, y = y,
                colour = year, linetype = year),
            size = line_sizee, geom = "line", position = "identity") +
  scale_linetype_manual(values = linev) +
  scale_colour_manual(values = legv) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1), limits = c(0, 0.25)) +
  # truncated at 25%!
  theme(legend.position = c(0.1,0.8))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_11_d.pdf"), plot)

``` 
