Data handling


# Start Inf data
# --------------

# read data
dtf <- read_dta("./Data/US/startinf_US.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 (for example with monthly data: date is on the 1st of
# each month)

# fix date
datelookup <- seq.Date(from = as.Date("1946-01-01"),
                       to = as.Date("2015-10-01"),
                       by = "quarter")
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(mean_mich = mean_mich/100,
                      variance_mich = variance_mich/(100^2),
                      skewness_mich = skewness_mich) %>% 
               mutate(milong_variance = milong_sd^2,
                      sd_mich = sqrt(variance_mich))

# Greenbook data (Philly FED)
# ---------------------------

# function to compute annual growth from 4 annualized qoq gr
qa2 <- function(x1,x2,x3,x4) {
  qa <- function(x) exp((1/4)*log(1+x))-1
  return((1+qa(x1))*(1+qa(x2))*(1+qa(x3))*(1+qa(x4))-1)
}

dtf <- dtf %>%  
      arrange(date) %>%
      mutate(gPGDPF4_annual = qa2(gPGDPF1,gPGDPF2,gPGDPF3,gPGDPF4))

# Compute forward rates from Livingston survey
# --------------------------------------------

for (n0 in 6) {
  
  q <- 3
  var0 <- paste0("liv_picpi",n0,"_median")
  
  for (nf in 12) {
    
    varf <- paste0("liv_picpi",nf,"_median")
    var1 <- paste0("liv_picpi_f_",n0,"_",nf)
      
    # forward rate
    dtf[,var1] <- ( (1 + dtf[,varf])^(nf/q) ) / ( (1 + dtf[,var0])^(n0/q) )
    dtf[,var1] <- exp( (1/((nf-n0)/q)) * 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/q)
    
  }
  
}

Error in [.data.frame(dtf, , var0) : undefined columns selected

Figures

Timespan is (mostly) 1965/06 to 1975/06

Figure 1: The evolution of the US economy

Subfigure 1(a): Actual Inflation


# actual inflation according to CPI, Core CPI, and GDP deflator between 1960 and 1980 

# vertical line width
vline_w <- 0.3 #mm

# text annotation high titles / mid titles
hfont <- 3.2
mfont <- 2
annotation_height <- 0.14

vars <- c("pi_12", "picore_12", "pigdp12")
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("CPI Inflation"
          ,"CPI Core Inflation"
          ,"GDP Deflator Inflation")

plot <- ggplot(dtf_long %>%
                 filter(date >= as.Date("1960-01-01") &
                        date <= as.Date("1980-12-31") &
                        variable %in% c("pi_12", "picore_12", "pigdp12") &
                        !is.na(value)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable),
                    size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                             breaks = seq(0,0.16,0.04),
                             limits = c(0,0.14)) +
          scale_x_date(date_breaks="1 year", date_labels = "%Y",
                       limits = c(as.Date("1960-01-01") ,as.Date("1980-12-31")),
                       expand = c(0.02,0)) +
          geom_vline(xintercept = as.numeric(as.Date("1965-05-01")),
                     linetype = "solid", size = tfont(vline_w)) +
          geom_vline(xintercept = as.numeric(as.Date("1971-08-01")),
                     linetype = "longdash", size = tfont(vline_w)) +
          geom_vline(xintercept = as.numeric(as.Date("1973-10-01")),
                     linetype = "longdash", size = tfont(vline_w)) +
          geom_vline(xintercept = as.numeric(as.Date("1974-12-01")),
                     linetype = "solid", size = tfont(vline_w)) +
          scale_linetype_manual(breaks = vars,
                                values = linev,
                                labels = legs) +
          scale_colour_manual(breaks = vars,
                              values = legv,
                              labels = legs) +
          theme(legend.position = c(0.112,0.5)) +
          annotate("text", x = as.Date("1962-06-30"), y = annotation_height,
                   label = "Anchor In Seabed", size = tfont(hfont)) +
          annotate("text", x = as.Date("1968-06-30"), y = annotation_height,
                   label = "A Drifting Anchor", size = tfont(hfont)) +
          annotate("text", x = as.Date("1972-09-01"), y = 0.07,
                   label = "USD Off", size = tfont(mfont)) +
          annotate("text", x = as.Date("1972-09-01"), y = 0.07-0.005,
                   label = "Gold", size = tfont(mfont)) +
          geom_segment(aes(x = as.Date("1972-09-01"), xend = as.Date("1972-02-01"),
                           y = 0.07-0.005-0.005, yend = 0.07-0.005-0.005 - 0.012),
                       arrow = arrow(length = unit(0.5, "cm"))) +
          annotate("text", x = as.Date("1974-05-01"), y = 0.14,
                   label = "First Oil", size = tfont(mfont)) +
          annotate("text", x = as.Date("1974-05-01"), y = 0.14-0.005,
                   label = "Shock", size = tfont(mfont))  +
          geom_segment(aes(x = as.Date("1974-05-01"),
                           xend = as.Date("1974-01-01"),
                           y = 0.14-0.005-0.005,
                           yend = 0.14-0.005-0.005 - 0.02),
                       arrow = arrow(length = unit(0.5, "cm"))) +
          annotate("text", x = as.Date("1977-11-01"), y = annotation_height,
                   label = "Unanchored Inflation", size = tfont(hfont)) +
          theme(legend.position = c(0.42,0.7)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))
        
print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_1_a.pdf"), plot)


# key numbers for paper
print(mean(dtf$pi_12[dtf$date >= as.Date("1966-01-01") &
                     dtf$date <= as.Date("1973-09-01")],
           na.rm = TRUE)
      )

[1] 0.04246377

# use beginning of quarter
print(dtf$pi_12[dtf$date == as.Date("1965-10-01")])

[1] 0.01923089

print(dtf$pi_12[dtf$date == as.Date("1973-07-01")])

[1] 0.06834541

Subfigure 1(b): Other macroeconomic series

fed funds, 10-year bonds, unemployment rate, federal deficit

with recession bars, 1964:1 to 1974:12


vars <- c("FEDFUNDS", "DGS10","UNRATE","FYFSDFYGDP")
legs <- c("Federal Funds Rate", "10-Year Treasury Yield", "Unemployment Rate", "Annual Federal Deficit (% GDP)")
legv <- create_legv(vars)
linev <- create_linev(vars)

plot <- ggplot(dtf_macro_long %>%
                 filter(variable %in% vars,
                        date >= as.Date("1964-01-01"),
                        date <= as.Date("1974-12-30")) %>%
                 # invert Federal Surplus to federal Deficit
                 mutate(value = ifelse(variable == "FYFSDFYGDP", -value, value)),
               aes(x = date, y = value)) +
  geom_line(aes(colour = variable,
                linetype = variable),
            size = line_size) +
  geom_hline(yintercept = 0) +
  scale_color_manual(breaks = vars, labels = legs, values = legv) +
  scale_linetype_manual(breaks = vars, labels = legs, values = linev) +
  rec_shade + # NBER Recession shading
  theme(legend.position = c(0.25,0.85)) +
  guides(colour = guide_legend(keywidth = 10, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_1_b.pdf"), plot)

Figure(s) 2


vars <- c("liv_picpi12_median",
          "spf_pigdp12_median",
          "gPGDPF4_annual"
          )

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Livingston median expected inflation",
          "SPF median expected inflation",
          "Greenbook inflation forecast"
         )

plot <- ggplot(dtf_long %>%
                 filter(date >= as.Date("1965-01-01") &
                        date <= as.Date("1975-06-30") &
                        variable %in% vars &
                        !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_2.pdf"), plot)

Figure(s) 3

expected inflation (median or mean) from Livingston,

from the SPF on GDP deflators,

and from greenbook between 1965 and 1975

Subfigure 3(a): Greenbook, FED staff


vars <- c("gPGDPF4_annual",
          "gPGDPF4",
          "gPGDPF1"
          )

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("12 months ahead",
          "9-12 months ahead",
          "3 months ahead"
          )

plot <- ggplot(dtf_long %>%
                 filter(date >= as.Date("1965-06-01") &
                        date <= as.Date("1975-06-30") &
                        variable %in% vars &
                        !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) + 
          theme(legend.position = c(0.4,0.8)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_3_a.pdf"), plot)

Subfigure 3(a): Livingston vs Forward


vars <- c("liv_picpi6_median",
          "liv_picpi12_median",
          "liv_picpi_f_6_12"
         )

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("12 months ahead median",
          "6 months ahead median",
          "6-12 months ahead"
         )

plot <- ggplot(dtf_long %>% 
                filter(date >= as.Date("1965-06-01") &
                       date <= as.Date("1975-06-30") &
                       variable %in% vars &
                       !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) + 
          theme(legend.position = c(0.3,0.8)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_3_b.pdf"), plot)

Figure(s) 4: Household qualitative expectations of inflation going up/down


vars <- c("milong_up", "milong_down")
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Percentage up",
          "Percentage down")

plot <- ggplot(dtf_long %>% 
                filter(date >= as.Date("1965-06-30") &
                       date <= as.Date("1975-06-30") &
                       variable %in% vars &
                       !is.na(value)) %>% 
                mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          theme(legend.position = c(0.2,0.5)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_4.pdf"), plot)

Figure(s) 5: Cross-sectional mean of expected inflation by households


vars <- c("mean_mich","milong_mean")
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Michigan quantitative survey mean",
          "Mankiw-Reis-Wolfers qualitative survey mean")

plot <- ggplot(dtf_long %>% 
                filter(date >= as.Date("1965-06-30") &
                       date <= as.Date("1975-06-30") &
                       variable %in% vars &
                       !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable)
                    , size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                             limits = c(0,0.12)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          theme(legend.position = c(0.35,0.9)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_5.pdf"), plot)

Figure(s) 6: Disagreement about expected inflation among households

Subfigure 6(a): Cross-sectional moments


vars <- c("sd_mich",
          "skewness_mich")

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Standard deviation (right axis)",
          "Skewness (left axis)")

df1  <- dtf_long %>% 
        filter(date >= as.Date("1965-06-30") &
               date <= as.Date("1975-06-30") &
               variable %in% "skewness_mich" &
               !is.na(value))

df2  <- dtf_long %>% 
        filter(date >= as.Date("1965-06-30") &
               date <= as.Date("1975-06-30") &
               variable %in% "sd_mich" &
               !is.na(value))

transf_b <- 37
transf_a <- 1.2

plot <- ggplot(df1, aes(x = date)) +
          geom_line(aes(y = value, colour = variable, linetype = variable),
                    size = line_size) +
          geom_line(aes(y = value*transf_b - transf_a, colour = variable, linetype = variable),
                    data = df2, size = line_size) +
          scale_y_continuous(sec.axis = sec_axis( trans = ~ (. + transf_a)/transf_b,
                                                  labels = scales::percent_format(accuracy = 1))
                            ) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          theme(legend.position = c(0.25,0.45)) +
          scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_6_a.pdf"), plot)

rm(df1, df2)

Subfigure 6(b): Three snapshots of the distribution


# 1967, 1070, 1974: Distribution of Household expectations
# get household distribution from earlier quantitative Michigan survey

dtf_michigan <- read_dta("./Data/US/michigan_moments.dta")

# handle dates
datelookup <- seq.Date(from = as.Date("1966-04-01"),
                       to = as.Date("1976-10-01"),
                       by = "quarter")
dtf_michigan$date <- datelookup[ dtf_michigan$date - min(dtf_michigan$date) + 1]

linev <- c("solid", "dashed", "dotted")
legv <- c('red','blue','green4')
legs <- c("1967", "1970", "1974")

breaks = c(1967, 1970, 1974)

dtf_michigan %<>%
  mutate(year = as.numeric(substr(as.character(date), 1, 4))) %>%
  filter(year %in% breaks) %>%
  rename(inf_downorsame = downorsame,
         inf_dontknowup = dontknowup) %>%
  # articifial data to make smooth curve
  mutate(inf_lower = 0,
         inf_higher = 0) %>%
  # to distribute mass of "dontknowup"
  mutate(mean_positive = (1.5*inf_1to2 + 3.5*inf_3to4 + 5*inf_5 + 7.5*inf_6to9 + 12*inf_10to14)/(inf_1to2 + inf_3to4 + inf_5 + inf_6to9 + inf_10to14)) %>%
  # for "those who "dontknow" use distribution as prior
  # i.e. ignore them
  pivot_longer(cols = starts_with("inf"),
               names_to = "x",
               values_to = "freq") %>%
  mutate(x = as.numeric(recode(x,
                    inf_lower = "-5",
                    inf_downorsame = "-2",
                    inf_1to2 = "1.5",
                    inf_3to4 = "3.5",
                    inf_5 = "5",
                    inf_6to9 = "7.5",
                    inf_10to14 = "12",
                    inf_higher = "17.5",
                    inf_dontknowup = as.character(mean_positive))),
         year = as.factor(year)) %>%
  select(-mean_positive) %>%
  group_by(year, x) %>%
  summarise(frequency = sum(freq),
            .groups = "drop") %>%
  group_by(year) %>%
  mutate(sum = sum(frequency),
         y = frequency/sum) %>%
  select(-c(sum, frequency))

plot <- ggplot(dtf_michigan,
               aes(x = x, y = y, col = year)) +
          stat_density(aes(x = x, weight = y,
                           colour = year, linetype = year),
                       inherit.aes = FALSE,
                       geom = "line",
                       position = "identity",
                       size = line_size,
                       bw = 1.3) +
          scale_x_discrete(limits = c(1.5, 3.5, 5, 7.5, 12)) +
          scale_colour_manual(breaks = breaks, values = legv, labels = legs) +
          scale_linetype_manual(breaks = breaks, values = linev, labels = legs) +
          theme(legend.position = c(0.1, 0.7)) +
          guides(col = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_6_b.pdf"), plot)

Figure(s) 8: Firm expectations of inflation


vars <- unique(dtf_long$variable[grepl("leew_", dtf_long$variable) &
                                 grepl("exp", dtf_long$variable)]
               )

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("% change in prices of goods and services sold",
          "% change in prices of capital goods purchased")

plot <- ggplot(dtf_long %>% 
                filter(variable %in% vars &
                       !is.na(value) &
                       date <= as.Date("1975-01-01")) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          theme(legend.position = c(0.4,0.85)) +
          scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_8.pdf"), plot)

Figure(s) 9: Gold prices, spot, per ounce


#plot
vars <- c("gold",
          "gold_price")
legv <- c("red","blue","black")
linev <- c("solid","longdash","dashed")
legs <- c("Zurich price",
          "London price",
          "USD to gold ounce convertability")

#USD GOLD PEG
peg <- 35

#position of segment
dtf_peg <- data.frame(x1 = as.Date("1966-03-30"),
                      x2 = as.Date("1971-08-13"),
                      y1 = peg, y2 = peg,
                      leg = "USD gold convertability")

plot <- ggplot(dtf_long %>%
                 filter(date >= as.Date("1966-03-30") &
                        date <= as.Date("1971-06-30") &
                        variable %in% vars &
                        !is.na(value)),
               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") +
          geom_segment(aes(x = x1, xend = x2, y = y1, yend = y2,
                           colour = leg, linetype = leg),
                       data = dtf_peg, size = 2) +
          scale_colour_manual(values = legv, labels = legs) +
          scale_linetype_manual(values = linev, labels = legs)  +
          theme(legend.position = c(0.2,0.9)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))
          
print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_9.pdf"), plot)
rm(dtf_peg)

Figure(s) 10 - 13: MATLAB or other countries

Figure(s) 14: Dropping the anchor: the US 1980s

Subfigure 14(a): Actual and survey first-order moments


dtf_MRW <- dtf
# clean original michigan values
input <- read_dta("./Data/US/MRWdata_0.dta")
input %<>% mutate(year = floor(date/100),
                  month = date %% 100,
                  quarter = lubridate::quarter(month)) %>%
  group_by(year, quarter) %>%
  summarise(mi_picpi12_mean_original = mean(mi_picpi12_mean),
            mi_picpi12_median_original = mean(mi_picpi12_median),
            .groups = "drop") %>%
  mutate(date = zoo::as.Date(zoo::as.yearqtr(year + ((quarter - 1)/4))))
  
# join
dtf_MRW %<>% left_join(input, by = "date") %>%
  select(-c(mi_picpi12_mean, mi_picpi12_median)) %>%
  rename(mi_picpi12_mean = mi_picpi12_mean_original,
         mi_picpi12_median = mi_picpi12_median_original) %>%
  select(date, spf_pigdp12_median, mi_picpi12_median)

# pivot
dtf_MRW %<>% pivot_longer(cols = !date,
                         names_to = "variable",
                         values_to = "value")
dtf_temp <- dtf_macro %>%
  select(date, CPIAUCSL, CPILFESL) %>%
  arrange(date) %>%
  mutate(inflation_CoreCPI = (CPIAUCSL - lag(CPIAUCSL, n = 4))/lag(CPIAUCSL, n = 4),
         inflation_CPI = (CPILFESL - lag(CPILFESL, n = 4))/lag(CPILFESL, n = 4)) %>%
  pivot_longer(cols = -date,
               names_to = "variable",
               values_to = "value")

dtf_dropping <- full_join(dtf_MRW, dtf_temp, by = c("date", "variable", "value")) %>%
  arrange(date, variable) %>%
  mutate(date = as.Date(date))

vars <- c("spf_pigdp12_median",
          "mi_picpi12_median",
          "inflation_CPI",
          "inflation_CoreCPI")
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("SPF median",
          "Michigan median",
          "Inflation (CPI)",
          "Inflation (Core CPI)")

plot <- ggplot(dtf_dropping %>%
                 filter(date >= as.Date("1978-01-01") &
                        date <= as.Date("1984-12-30") &
                        variable %in% vars &
                        !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 0.1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          theme(legend.position = c(0.2,0.2)) +
          guides(linetype = guide_legend(keywidth = 5, keyheight = 1))


print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_14_a.pdf"), plot)
rm(dtf_MRW, dtf_temp)

Subfigure 14(b): Survey disagreement


# 1979 - 1985: Disagreement, Skewness for michigan; based on MRW data

# disagreement: standard deviation
# skewness: build from percentiles

vars_right <- c("mi_picpi12_sd")
vars_left <- c("mi_picpi12_skewness")
vars <- c(vars_right, vars_left)
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Standard deviation (right axis)",
          "Skewness (left axis)")

transf <- 20

dtf_temp <- dtf_long %>%
  filter(date >= as.Date("1979-01-01") &
         date <= as.Date("1984-12-30") &
         variable %in% vars &
         !is.na(value)) %>%
  mutate(variable = factor(variable, levels = vars))

plot <- ggplot(dtf_temp %>%
                 filter(variable %in% c(vars_left)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable,
                        linetype = variable),
                    size = line_size) +
          geom_line(data = dtf_temp %>%
                      filter(variable %in% c(vars_right)),
                    aes(x = date, y = transf * value,
                        colour = variable, linetype = variable,
                        order = as.numeric(variable)),
                    size = line_size) +
          scale_y_continuous(sec.axis = sec_axis(trans = ~ (. / transf),
                             labels = scales::percent_format(accuracy = 1))
                            ) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          theme(legend.position = c(0.4,0.9)) +
          guides(linetype = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_14_b.pdf"), plot)
rm(dtf_temp)

Figure(s) 15: The expected inflation anchor through the pandemic

Subfigure 15(a): Actual inflation


time_series_files <- list.files("./Data/US/US_macrodata", pattern = "*.xls", full.names = TRUE)
dtf_inflation <- as.data.frame(seq.Date(from = as.Date("2016-01-01"),
                                           to = as.Date("2021-07-01"),
                                           by = "month"))
colnames(dtf_inflation) <- "date"

# clean and merge all files in the macrodata-directory
for (filename in time_series_files) {
  if (grepl("USREC", filename)) next
    skip <- 10
    input <- read_xls(filename, skip = skip)
    type_freq <- colnames(read_xls(filename, range = "A10"))
    type_freq <- tolower(gsub("Frequency: ", "", type_freq))
    
    value_name <- gsub(".xls", "", gsub("./Data/US/US_macrodata/", "", filename))
    colnames(input) <- c("date", "value")
    # determine frequency, take means over quarter if necessary
    
    if (type_freq %in% c("monthly")) { # monthly
      input %<>% rename(!!value_name := value)
      dtf_inflation %<>% left_join(input, by = "date")
    }
}

New names: * 0 -> 0...2 * 5.0999999999999996 -> 5.0999999999999996...4 * 5.0999999999999996 -> 5.0999999999999996...5 * 0.29999999999999999 -> 0.29999999999999999...8 * 0 -> 0...9 * …

dtf_inflation %<>%
  mutate(date = as.Date(date),
         CPIAUCSL = 100*( (CPIAUCSL - lag(CPIAUCSL, 12))/lag(CPIAUCSL, 12)),
         CPILFESL = 100*( (CPILFESL - lag(CPILFESL, 12))/lag(CPILFESL, 12)))

dtf_inflation %<>% pivot_longer(cols = -any_of("date"),
                                names_to = "variable",
                                values_to = "value")

vars <- c("CPILFESL", "CPIAUCSL", "PCETRIM12M159SFRBDAL")
linev <- c(create_linev(vars), "solid")
legv <- c(create_legv(vars), "black")
legs <- c("CPI Urban Core", "CPI Urban All Goods", "Trimmed Mean PCE", "Inflation Target")

plot <- ggplot(dtf_inflation %>%
                   filter(variable %in% vars,
                          date >= as.Date("2018-01-01"),
                          date <= as.Date("2021-07-01")) %>%
                   mutate(variable = as.factor(variable)),
                 aes(x = date, y = value)) +
  geom_hline(aes(col = "Inflation Target",
                 linetype = "Inflation Target"),
             yintercept = 2, size = line_size) +
  geom_line(aes(col = variable, linetype = variable),
            size = line_size) +
  scale_y_continuous(labels = function(x) paste(x, "%"), limits = c(-1, 6), breaks = seq(0, 6, by = 1)) +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  scale_linetype_manual(values = linev, labels = legs) +  
  scale_colour_manual(values = legv, labels = legs) +
  theme(legend.position = c(0.5,0.9)) +
  guides(linetype = guide_legend(keywidth = 7, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_15_a.pdf"), plot)

Subfigure 15(b): Markets and survey first-order moments


dtf_households <- read_xlsx("./Data/US/2_Michigan.xlsx", sheet = "Michigan", skip = 2)
dtf_households %<>% rename(date = Date) %>%
  mutate(date = as.Date(date),
         year = year(date),
         quarter = quarter(date)) %>%
  group_by(year, quarter) %>%
  summarise(date = first(date),
            across(starts_with("pie_michigan"), mean),
            .groups = "drop") %>%
  # skew is Pearson's 2nd nonparametric skew coefficient
  mutate(pie_michigan_sd_1y = 3*(pie_michigan_mean_1y -pie_michigan_median_1y) / pie_michigan_skew_1y,
         pie_michigan_sd_5y = 3*(pie_michigan_mean_5y -pie_michigan_median_5y) / pie_michigan_skew_5y)

# read and clean SPF one-year ahead forecast
dtf_nonhouseholds <- read_xlsx("./Data/US/Individual_CPI.xlsx")
dtf_nonhouseholds %<>%
  rename_with(tolower) %>%
  rename(pie_people_1y = cpi6) %>%
  mutate(date = year + ((quarter - 1)/4),
         date = zoo::as.Date(zoo::as.yearqtr(date))) %>%
  select(date, year, quarter, id, industry, pie_people_1y) %>%
  filter(date >= as.Date("2018-01-01")) %>%
  mutate(pie_people_1y = ifelse(pie_people_1y == "#N/A", NA, pie_people_1y),
         pie_people_1y = as.numeric(pie_people_1y),
         industry = as.factor(industry))
levels(dtf_nonhouseholds$industry) <- c("fin", "bus", "unknown")

dtf_nonhouseholds_all <- dtf_nonhouseholds %>%
  group_by(year, quarter) %>%
  summarise(date = first(date),
            pie_people_1y_all_mean = mean(pie_people_1y, na.rm = TRUE),
            pie_people_1y_all_sd = sd(pie_people_1y, na.rm = TRUE),
            pie_people_1y_all_median = median(pie_people_1y, na.rm = TRUE),
            pie_people_1y_all_interq = IQR(pie_people_1y, na.rm = TRUE),
            pie_people_1y_all_skew = moments::skewness(pie_people_1y, na.rm = TRUE),
            .groups = "drop") %>%
  select(-c(year, quarter))

dtf_nonhouseholds_fin <- dtf_nonhouseholds %>%
  filter(as.integer(industry) == 1) %>%
  group_by(year, quarter) %>%
  summarise(date = first(date),
            pie_people_1y_fin_mean = mean(pie_people_1y, na.rm = TRUE),
            pie_people_1y_fin_sd = sd(pie_people_1y, na.rm = TRUE),
            pie_people_1y_fin_median = median(pie_people_1y, na.rm = TRUE),
            pie_people_1y_fin_interq = IQR(pie_people_1y, na.rm = TRUE),
            pie_people_1y_fin_skew = moments::skewness(pie_people_1y, na.rm = TRUE),
            .groups = "drop") %>%
  select(-c(year, quarter))

dtf_nonhouseholds_bus <- dtf_nonhouseholds %>%
  filter(as.integer(industry) == 2) %>%
  group_by(year, quarter) %>%
  summarise(date = first(date),
            pie_people_1y_bus_mean = mean(pie_people_1y, na.rm = TRUE),
            pie_people_1y_bus_sd = sd(pie_people_1y, na.rm = TRUE),
            pie_people_1y_bus_median = median(pie_people_1y, na.rm = TRUE),
            pie_people_1y_bus_interq = IQR(pie_people_1y, na.rm = TRUE),
            pie_people_1y_bus_skew = moments::skewness(pie_people_1y, na.rm = TRUE),
            .groups = "drop") %>%
  select(-c(year, quarter))
  
dtf_nonhouseholds_all %<>%
  left_join(dtf_nonhouseholds_fin, by = "date") %>%
  left_join(dtf_nonhouseholds_bus, by = "date")

# join with Household Survey (Michigan survey of Consumers)
dtf_modern <- left_join(dtf_nonhouseholds_all, dtf_households, by = "date")
dtf_modern %<>% left_join(dtf_macro %>% select(date, T10YIE), by = "date")

dtf_modern %<>%
  pivot_longer(cols = -date,
               names_to = "variable",
               values_to = "value")

vars <- c("pie_michigan_mean_1y", "pie_michigan_mean_5y", "pie_people_1y_all_mean", "T10YIE")
linev <- create_linev(vars)
legv <- create_legv(vars)
legs <- c("People: Michigan 1 Year", "People: Michigan 5 Years", "Traders: SPF 1 Year", "Market: 10 year Breakeven Inflation Rate")

plot <- ggplot(dtf_modern %>%
                 filter(variable %in% vars) %>%
                 mutate(variable = as.factor(variable)),
               aes(x = date, y = value)) +
  geom_line(aes(col = variable, linetype = variable),
            size = line_size) +
  scale_y_continuous(labels = function(x) paste(x, "%")) +
  scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
  scale_colour_manual(breaks = vars, values = legv, labels = legs) +
  theme(legend.position = c(0.5,0.7)) +
  guides(linetype = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_15_b.pdf"), plot)

Subfigure 15(c): Cross-sectional disagreement of households


vars_left <- c("pie_michigan_skew_1y")
vars_right <- c("pie_michigan_sd_1y")
vars <- c(vars_right, vars_left)
legv <- c('red', 'blue')
linev <- c('solid', 'longdash')
legs <- c("Standard deviation (right axis)", "Skewness (left axis)")

plot <- ggplot(dtf_modern %>%
                 filter(variable %in% vars_left) %>%
                 mutate(variable = as.factor(variable)),
               aes(x = date, y = value)) +
  geom_line(aes(col = variable, linetype = variable),
            size = line_size) +
  geom_line(data = dtf_modern %>%
              filter(variable %in% vars_right) %>%
              mutate(variable = as.factor(variable)),
            aes(x = date, y = value / 4, col = variable, linetype = variable),
            size = line_size) +
  scale_y_continuous(sec.axis = sec_axis(trans = ~ . * 4)) +
  scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
  scale_colour_manual(breaks = vars, values = legv, labels = legs) +
  theme(legend.position = c(0.3,0.8)) +
  guides(linetype = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_15_c.pdf"), plot)

Plot 4: Distribution of Household Expectations across different months (not quarters, unlike A3!)


# Build monthly histogram of Household Inflation Expectation
dtf_histogram <- read_xlsx("./Data/US/2_Michigan.xlsx", sheet = "Next year", skip = 2)

dtf_histogram %<>%
  rename_with(tolower) %>%
  select(year, month, down, same, starts_with("up"), starts_with("dk")) %>%
  mutate(inf_lowerbound = 0, # fix tails
         inf_upperbound = 0) %>%
  rename(inf_down = down,
         inf_0 = same,
         inf_1to2 = `up by 1-2%`,
         inf_3to4 = `up by 3-4%`,
         inf_5 = `up by 5%`,
         inf_6to9 = `up by 6-9%`,
         inf_10to14 = `up by 10-14%`,
         inf_15plus = `up by 15%+`,
         inf_dontknowup = `up; dk how much`,
         inf_dontknow = `dk; na`
         ) %>%
  mutate(mean_positive = (1.5*inf_1to2 + 3.5*inf_3to4 + 5*inf_5 + 7.5*inf_6to9 + 12*inf_10to14 + 17*inf_15plus)/(inf_1to2 + inf_3to4 + inf_5 + inf_6to9 + inf_10to14 + inf_15plus)) %>% # deal with "dontknowup"
  select(-inf_dontknow) %>% # use common distribution as prior for "dontknow"
  pivot_longer(cols = starts_with("inf"),
               names_to = "x",
               values_to = "freq") %>%
  mutate(x = as.numeric(recode(x,
                    inf_lowerbound = "-5",
                    inf_down = "-2",
                    inf_0 = "0",
                    inf_1to2 = "1.5",
                    inf_3to4 = "3.5",
                    inf_5 = "5",
                    inf_6to9 = "7.5",
                    inf_10to14 = "12",
                    inf_15plus = "17",
                    inf_upperbound = "20",
                    inf_dontknowup = as.character(mean_positive)))) %>%
  select(-mean_positive) %>%
  # turn frequencies into probabilities
  group_by(year, month) %>%
  mutate(sum = sum(freq),
         y = freq /sum) %>%
  select(-c(sum, freq)) %>%
  mutate(date = year + ((month - 1)/12),
         date = zoo::as.Date(zoo::yearmon(date)),
         group = 0,
         group = ifelse(year == 2020 & month == 1, 1, group),
         group = ifelse(year == 2020 & month == 9, 2, group),
         group = ifelse(year == 2021 & month == 6, 3, group))

linev <- c("solid", "dashed", "dotted")
legv <- c('red', 'blue', 'green4')
legs <- c("2020 January", "2020 September", "2021 June")
breaks = c(1, 2, 3)

plot <- ggplot(dtf_histogram %>%
                 filter(group %in% breaks) %>%
                 mutate(group = as.factor(group)),
               aes(x = x, y = y, col = group)) +
          stat_density(aes(x = x, weight = y,
                           colour = group, linetype = group),
                       inherit.aes = FALSE,
                       geom = "line",
                       position = "identity",
                       size = line_size,
                       bw = 1.3) +
          scale_x_discrete(limits = c(0, 1.5, 3.5, 5, 7.5, 12)) +
          scale_colour_manual(breaks = breaks, values = legv, labels = legs) +
          scale_linetype_manual(breaks = breaks, values = linev, labels = legs) +
          theme(legend.position = c(0.6, 0.7)) +
          guides(col = guide_legend(keywidth = 5, keyheight = 1))

print(plot)

if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_15_d.pdf"), plot)
---
title: "Brookings Paper - Figures - United States - 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 (quarterly), be careful for e.g. quarterly data
bAdjustPeriodTiming <- 1   # 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
              ,"colorspace" # for custom colouring plots
              ,"magrittr"   # pipe operator
              ,"moments"    # skewness
)

# 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}

# 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 <- 10 # mm

# size of line for plots
line_size <- 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','orange','orange', 'yellow', 'purple', 'black')
  n <- length(x)
  y <- y[1:n]
  names(y) <- x 
  return(y)
}

create_linev <- function(x) {
  #colours to be used in plots
  y <- c('solid','longdash','dotted','dotdash')
  n <- length(x)
  y <- y[1:n]
  names(y) <- x 
  return(y)
}

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/US/startinf_US.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 (for example with monthly data: date is on the 1st of
# each month)

# fix date
datelookup <- seq.Date(from = as.Date("1946-01-01"),
                       to = as.Date("2015-10-01"),
                       by = "quarter")
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(mean_mich = mean_mich/100,
                      variance_mich = variance_mich/(100^2),
                      skewness_mich = skewness_mich) %>% 
               mutate(milong_variance = milong_sd^2,
                      sd_mich = sqrt(variance_mich))

# Greenbook data (Philly FED)
# ---------------------------

# function to compute annual growth from 4 annualized qoq gr
qa2 <- function(x1,x2,x3,x4) {
  qa <- function(x) exp((1/4)*log(1+x))-1
  return((1+qa(x1))*(1+qa(x2))*(1+qa(x3))*(1+qa(x4))-1)
}

dtf <- dtf %>%  
      arrange(date) %>%
      mutate(gPGDPF4_annual = qa2(gPGDPF1,gPGDPF2,gPGDPF3,gPGDPF4))

# Compute forward rates from Livingston survey
# --------------------------------------------

for (n0 in 6) {
  
  q <- 3
  var0 <- paste0("liv_picpi",n0,"_median")
  
  for (nf in 12) {
    
    varf <- paste0("liv_picpi",nf,"_median")
    var1 <- paste0("liv_picpi_f_",n0,"_",nf)
      
    # forward rate
    dtf[,var1] <- ( (1 + dtf[,varf])^(nf/q) ) / ( (1 + dtf[,var0])^(n0/q) )
    dtf[,var1] <- exp( (1/((nf-n0)/q)) * 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/q)
    
  }
  
}

# 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("gPGDPF4"),
                   "liv_picpi12_median",
                   starts_with("spf"),
                   ends_with("mich"),
                   starts_with("milong"),
                   starts_with("mi")),
              ~ lag(.,4) ) %>%
    mutate(gPGDPF1 = lag(gPGDPF1, 1),
           gPGDPF2 = lag(gPGDPF2, 2),
           gPGDPF3 = lag(gPGDPF3, 3),
           liv_picpi6_median = lag(liv_picpi6_median, 2) #quarterly data
           # notice there is no "-1" as the raw data is end of period
           )
}
 
# data is annual so putting it in beginning of year
dtf <- dtf %>%
  mutate_at(vars(starts_with("leew_"),
                 starts_with("proquest")),
            ~lead(., 3))
 
# Fischer-Huizinga data
# ---------------------

# import .dta file (as Ricardo asked to be separate)
dtf9 <- read_dta("Data/US/fischer_huizinga.dta")

# take care of date
datelookup <- seq.Date(as.Date("1939-01-01"),
                       as.Date("1978-12-01"),
                       by = "month")

dtf9$date <- datelookup[dtf9$date - min(dtf9$date) + 1]

# quarterly instead of monthly frequency
dtf9$yq <- zoo::as.yearqtr(dtf9$date)

dtf9 <- dtf9 %>% 
  group_by(yq) %>%
  summarize(date = first(date),
            inflation = mean(inflation/100, na.rm = TRUE),
            unemployment = mean(unemployment/100, na.rm = TRUE)) %>%
  select(-yq) %>% 
  mutate(inflation_minus_employment = inflation - unemployment) %>% 
  rename_at(vars(-date), ~ paste0("gallup_",.))

# merge the two
dtf <- merge(dtf, dtf9, by = "date", all = TRUE)
rm(dtf9)

# plot

# skewness from Michigan survey-percentiles
# -----------------------------------------

# take midpoint of bins from empirical cdf to estimate skewness
dtf$mi_picpi12_skewness <- with(dtf,
                     ((1/0.98) *
                       (0.04 * (mi_picpi12_5 -  mi_picpi12_1) ^3 +
                        0.05 * (mi_picpi12_10 - mi_picpi12_5) ^3 +
                        0.1 *  (mi_picpi12_20 - mi_picpi12_10)^3 +
                        0.05 * (mi_picpi12_25 - mi_picpi12_20)^3 +
                        0.08 * (mi_picpi12_33 - mi_picpi12_25)^3 +
                        0.17 * (mi_picpi12_50 - mi_picpi12_33)^3 +
                        0.17 * (mi_picpi12_67 - mi_picpi12_50)^3 +
                        0.08 * (mi_picpi12_75 - mi_picpi12_67)^3 +
                        0.05 * (mi_picpi12_80 - mi_picpi12_75)^3 +
                        0.1 *  (mi_picpi12_90 - mi_picpi12_80)^3 +
                        0.05 * (mi_picpi12_95 - mi_picpi12_90)^3 +
                        0.04 * (mi_picpi12_99 - mi_picpi12_95)^3
                       )) /
                     ((1/0.98) *
                        (
                        0.04 * (mi_picpi12_5 -  mi_picpi12_1) ^2 +
                        0.05 * (mi_picpi12_10 - mi_picpi12_5) ^2 +
                        0.1 *  (mi_picpi12_20 - mi_picpi12_10)^2 +
                        0.05 * (mi_picpi12_25 - mi_picpi12_20)^2 +
                        0.08 * (mi_picpi12_33 - mi_picpi12_25)^2 +
                        0.17 * (mi_picpi12_50 - mi_picpi12_33)^2 +
                        0.17 * (mi_picpi12_67 - mi_picpi12_50)^2 +
                        0.08 * (mi_picpi12_75 - mi_picpi12_67)^2 +
                        0.05 * (mi_picpi12_80 - mi_picpi12_75)^2 +
                        0.1 *  (mi_picpi12_90 - mi_picpi12_80)^2 +
                        0.05 * (mi_picpi12_95 - mi_picpi12_90)^2 +
                        0.04 * (mi_picpi12_99 - mi_picpi12_95)^2
                        ))^(3/2)
                    )
 
# pivot data to long format
dtf_long <- pivot_longer(dtf %>% select(-d),
                         !date, names_to = "variable", values_to = "value",
                         values_transform = as.numeric)

# read macroeconomic data

time_series_files <- list.files("./Data/US/US_macrodata", pattern = "*.xls", full.names = TRUE)
dtf_macro <- as.data.frame(seq.Date(from = as.Date("1946-01-01"),
                                    to = as.Date("2021-10-01"),
                                    by = "quarter"))
colnames(dtf_macro) <- "date"

# clean and merge all files in the macrodata-directory
for (filename in time_series_files) {
  if (!grepl("RomerandRomer", filename)) {
    # FRED series, read data
    skip <- 10
    if (grepl("USREC", filename)) skip <- 1211
    input <- read_xls(filename, skip = skip)
    value_name <- gsub(".xls", "", gsub("./Data/US/US_macrodata/", "", filename))
    colnames(input) <- c("date", "value")
    # determine frequency, take means over quarter if necessary
    type_freq <- colnames(read_xls(filename, range = "A10"))
    type_freq <- tolower(gsub("Frequency: ", "", type_freq))
    
    if (type_freq %in% c("monthly", "daily")) { # monthly, daily
      input %<>%
        mutate(year = year(date),
               quarter = quarter(date))
    
      if (value_name == "USREC") {
      input %<>%
          group_by(year, quarter) %>%
          summarise(value = max(value),
                  .groups = "drop")
      } else {
        input %<>%
          group_by(year, quarter) %>%
          summarise(value = mean(value),
                  .groups = "drop")
      }
      input %<>%
        mutate(temp = paste0(as.character(year), " Q", as.character(quarter)),
               date = zoo::as.Date(zoo::as.yearqtr(temp, format = "%Y Q%q"))) %>%
        select(date, value) %>%
        rename(!!value_name := value)
      dtf_macro %<>% left_join(input, by = "date")
    }
    
    if (type_freq %in% c("annual, fiscal year")) { # annual
      input %<>%
        mutate(year = year(date)) %>%
        select(year, value) %>%
        rename(!!value_name := value)
      
      dtf_macro %<>%
        mutate(year = year(date)) %>%
        left_join(input, by = "year") %>%
        select(-year)
    }
    
    if (type_freq %in% c("quarterly")) { # quarterly
      input %<>% rename(!!value_name := value)
      dtf_macro %<>% left_join(input, by = "date")
    }
    
  } else { # Romer and Romer data
    input <- read_xls(filename, sheet = "DATA BY MONTH")
    input %<>%
      rename(date = DATE) %>%
      select(date, RESID) %>%
      mutate(year = year(date),
             quarter = quarter(date)) %>%
      group_by(year, quarter) %>%
      summarise(date = first(date),
                RESID = mean(RESID),
                .groups = "drop") %>%
      select(date, RESID)
    dtf_macro %<>% left_join(input, by = "date")
  }
}

plot_start <- as.Date("1964-01-01")
plot_end <- as.Date("1974-12-30")

# change recession indicator to start/end (adapted from Fabian Scheler under https://rpubs.com/FSl/609471)
dtf_macro$recession_diff <- dtf_macro$USREC - lag(dtf_macro$USREC)
recession_start <- dtf_macro[dtf_macro$recession_diff == 1 & !is.na(dtf_macro$recession_diff), ]$date
recession_end   <- dtf_macro[dtf_macro$recession_diff == -1 & !is.na(dtf_macro$recession_diff),]$date

recession_start <- recession_start[recession_start >= plot_start & recession_start <= plot_end]
recession_end   <- recession_end[recession_end >= plot_start & recession_end <= plot_end]

if(length(recession_start) > length(recession_end)) {
  recession_end <- c(recession_end, as.POSIXct(plot_end))
}
if(length(recession_end) > length(recession_start)) {
  recession_start <- c(as.POSIXct(plot_start), recession_start)
}
recs <- data.frame(recession_start = recession_start, recession_end = recession_end)
if (nrow(recs) > 0) {
  rec_shade <- geom_rect(data = recs, inherit.aes = FALSE,
                         aes(xmin = recession_start, xmax = recession_end,
                             ymin = -Inf, ymax = Inf), alpha = 0.5) # fill, alpha 0.5?
}


dtf_macro %<>%
  mutate(GDPC1 = 0.1*(GDPC1 - lag(GDPC1)),
         INDPRO = 0.1*(INDPRO - lag(INDPRO)))
dtf_macro_long <- dtf_macro %>%
  pivot_longer(cols = -any_of("date"),
               names_to = "variable",
               values_to = "value")

```

# Figures
#### Timespan is (mostly) 1965/06 to 1975/06

## Figure 1: The evolution of the US economy
## Subfigure 1(a): Actual Inflation

```{r fig_1_a}

# actual inflation according to CPI, Core CPI, and GDP deflator between 1960 and 1980 

# vertical line width
vline_w <- 0.3 #mm

# text annotation high titles / mid titles
hfont <- 3.2
mfont <- 2
annotation_height <- 0.14

vars <- c("pi_12", "picore_12", "pigdp12")
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("CPI Inflation"
          ,"CPI Core Inflation"
          ,"GDP Deflator Inflation")

plot <- ggplot(dtf_long %>%
                 filter(date >= as.Date("1960-01-01") &
                        date <= as.Date("1980-12-31") &
                        variable %in% c("pi_12", "picore_12", "pigdp12") &
                        !is.na(value)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable),
                    size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                             breaks = seq(0,0.16,0.04),
                             limits = c(0,0.14)) +
          scale_x_date(date_breaks="1 year", date_labels = "%Y",
                       limits = c(as.Date("1960-01-01") ,as.Date("1980-12-31")),
                       expand = c(0.02,0)) +
          geom_vline(xintercept = as.numeric(as.Date("1965-05-01")),
                     linetype = "solid", size = tfont(vline_w)) +
          geom_vline(xintercept = as.numeric(as.Date("1971-08-01")),
                     linetype = "longdash", size = tfont(vline_w)) +
          geom_vline(xintercept = as.numeric(as.Date("1973-10-01")),
                     linetype = "longdash", size = tfont(vline_w)) +
          geom_vline(xintercept = as.numeric(as.Date("1974-12-01")),
                     linetype = "solid", size = tfont(vline_w)) +
          scale_linetype_manual(breaks = vars,
                                values = linev,
                                labels = legs) +
          scale_colour_manual(breaks = vars,
                              values = legv,
                              labels = legs) +
          theme(legend.position = c(0.112,0.5)) +
          annotate("text", x = as.Date("1962-06-30"), y = annotation_height,
                   label = "Anchor In Seabed", size = tfont(hfont)) +
          annotate("text", x = as.Date("1968-06-30"), y = annotation_height,
                   label = "A Drifting Anchor", size = tfont(hfont)) +
          annotate("text", x = as.Date("1972-09-01"), y = 0.07,
                   label = "USD Off", size = tfont(mfont)) +
          annotate("text", x = as.Date("1972-09-01"), y = 0.07-0.005,
                   label = "Gold", size = tfont(mfont)) +
          geom_segment(aes(x = as.Date("1972-09-01"), xend = as.Date("1972-02-01"),
                           y = 0.07-0.005-0.005, yend = 0.07-0.005-0.005 - 0.012),
                       arrow = arrow(length = unit(0.5, "cm"))) +
          annotate("text", x = as.Date("1974-05-01"), y = 0.14,
                   label = "First Oil", size = tfont(mfont)) +
          annotate("text", x = as.Date("1974-05-01"), y = 0.14-0.005,
                   label = "Shock", size = tfont(mfont))  +
          geom_segment(aes(x = as.Date("1974-05-01"),
                           xend = as.Date("1974-01-01"),
                           y = 0.14-0.005-0.005,
                           yend = 0.14-0.005-0.005 - 0.02),
                       arrow = arrow(length = unit(0.5, "cm"))) +
          annotate("text", x = as.Date("1977-11-01"), y = annotation_height,
                   label = "Unanchored Inflation", size = tfont(hfont)) +
          theme(legend.position = c(0.42,0.7)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))
        
print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_1_a.pdf"), plot)


# key numbers for paper
print(mean(dtf$pi_12[dtf$date >= as.Date("1966-01-01") &
                     dtf$date <= as.Date("1973-09-01")],
           na.rm = TRUE)
      )

# use beginning of quarter
print(dtf$pi_12[dtf$date == as.Date("1965-10-01")])
print(dtf$pi_12[dtf$date == as.Date("1973-07-01")])

```
## Subfigure 1(b): Other macroeconomic series
## fed funds, 10-year bonds, unemployment rate, federal deficit
## with recession bars, 1964:1 to 1974:12

```{r fig_1_b}

vars <- c("FEDFUNDS", "DGS10","UNRATE","FYFSDFYGDP")
legs <- c("Federal Funds Rate", "10-Year Treasury Yield", "Unemployment Rate", "Annual Federal Deficit (% GDP)")
legv <- create_legv(vars)
linev <- create_linev(vars)

plot <- ggplot(dtf_macro_long %>%
                 filter(variable %in% vars,
                        date >= as.Date("1964-01-01"),
                        date <= as.Date("1974-12-30")) %>%
                 # invert Federal Surplus to federal Deficit
                 mutate(value = ifelse(variable == "FYFSDFYGDP", -value, value)),
               aes(x = date, y = value)) +
  geom_line(aes(colour = variable,
                linetype = variable),
            size = line_size) +
  geom_hline(yintercept = 0) +
  scale_color_manual(breaks = vars, labels = legs, values = legv) +
  scale_linetype_manual(breaks = vars, labels = legs, values = linev) +
  rec_shade + # NBER Recession shading
  theme(legend.position = c(0.25,0.85)) +
  guides(colour = guide_legend(keywidth = 10, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_1_b.pdf"), plot)


```

## Figure(s) 2

```{r fig_2}

vars <- c("liv_picpi12_median",
          "spf_pigdp12_median",
          "gPGDPF4_annual"
          )

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Livingston median expected inflation",
          "SPF median expected inflation",
          "Greenbook inflation forecast"
         )

plot <- ggplot(dtf_long %>%
                 filter(date >= as.Date("1965-01-01") &
                        date <= as.Date("1975-06-30") &
                        variable %in% vars &
                        !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_2.pdf"), plot)

```

## Figure(s) 3

# expected inflation (median or mean) from Livingston,
# from the SPF on GDP deflators,
# and from greenbook between 1965 and 1975

## Subfigure 3(a): Greenbook, FED staff

```{r fig_3_a}

vars <- c("gPGDPF4_annual",
          "gPGDPF4",
          "gPGDPF1"
          )

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("12 months ahead",
          "9-12 months ahead",
          "3 months ahead"
          )

plot <- ggplot(dtf_long %>%
                 filter(date >= as.Date("1965-06-01") &
                        date <= as.Date("1975-06-30") &
                        variable %in% vars &
                        !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) + 
          theme(legend.position = c(0.4,0.8)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_3_a.pdf"), plot)

```

## Subfigure 3(a): Livingston vs Forward

```{r fig_3_b}

vars <- c("liv_picpi6_median",
          "liv_picpi12_median",
          "liv_picpi_f_6_12"
         )

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("12 months ahead median",
          "6 months ahead median",
          "6-12 months ahead"
         )

plot <- ggplot(dtf_long %>% 
                filter(date >= as.Date("1965-06-01") &
                       date <= as.Date("1975-06-30") &
                       variable %in% vars &
                       !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) + 
          theme(legend.position = c(0.3,0.8)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_3_b.pdf"), plot)


```

## Figure(s) 4: Household qualitative expectations of inflation going up/down

```{r fig_4}

vars <- c("milong_up", "milong_down")
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Percentage up",
          "Percentage down")

plot <- ggplot(dtf_long %>% 
                filter(date >= as.Date("1965-06-30") &
                       date <= as.Date("1975-06-30") &
                       variable %in% vars &
                       !is.na(value)) %>% 
                mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          theme(legend.position = c(0.2,0.5)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_4.pdf"), plot)

```

## Figure(s) 5: Cross-sectional mean of expected inflation by households

```{r fig_5}

vars <- c("mean_mich","milong_mean")
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Michigan quantitative survey mean",
          "Mankiw-Reis-Wolfers qualitative survey mean")

plot <- ggplot(dtf_long %>% 
                filter(date >= as.Date("1965-06-30") &
                       date <= as.Date("1975-06-30") &
                       variable %in% vars &
                       !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable)
                    , size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                             limits = c(0,0.12)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          theme(legend.position = c(0.35,0.9)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_5.pdf"), plot)

```

## Figure(s) 6: Disagreement about expected inflation among households
## Subfigure 6(a): Cross-sectional moments

```{r fig_6_a}

vars <- c("sd_mich",
          "skewness_mich")

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Standard deviation (right axis)",
          "Skewness (left axis)")

df1  <- dtf_long %>% 
        filter(date >= as.Date("1965-06-30") &
               date <= as.Date("1975-06-30") &
               variable %in% "skewness_mich" &
               !is.na(value))

df2  <- dtf_long %>% 
        filter(date >= as.Date("1965-06-30") &
               date <= as.Date("1975-06-30") &
               variable %in% "sd_mich" &
               !is.na(value))

transf_b <- 37
transf_a <- 1.2

plot <- ggplot(df1, aes(x = date)) +
          geom_line(aes(y = value, colour = variable, linetype = variable),
                    size = line_size) +
          geom_line(aes(y = value*transf_b - transf_a, colour = variable, linetype = variable),
                    data = df2, size = line_size) +
          scale_y_continuous(sec.axis = sec_axis( trans = ~ (. + transf_a)/transf_b,
                                                  labels = scales::percent_format(accuracy = 1))
                            ) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          theme(legend.position = c(0.25,0.45)) +
          scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_6_a.pdf"), plot)

rm(df1, df2)

```

## Subfigure 6(b): Three snapshots of the distribution

```{r fig_6_b}

# 1967, 1070, 1974: Distribution of Household expectations
# get household distribution from earlier quantitative Michigan survey

dtf_michigan <- read_dta("./Data/US/michigan_moments.dta")

# handle dates
datelookup <- seq.Date(from = as.Date("1966-04-01"),
                       to = as.Date("1976-10-01"),
                       by = "quarter")
dtf_michigan$date <- datelookup[ dtf_michigan$date - min(dtf_michigan$date) + 1]

linev <- c("solid", "dashed", "dotted")
legv <- c('red','blue','green4')
legs <- c("1967", "1970", "1974")

breaks = c(1967, 1970, 1974)

dtf_michigan %<>%
  mutate(year = as.numeric(substr(as.character(date), 1, 4))) %>%
  filter(year %in% breaks) %>%
  rename(inf_downorsame = downorsame,
         inf_dontknowup = dontknowup) %>%
  # articifial data to make smooth curve
  mutate(inf_lower = 0,
         inf_higher = 0) %>%
  # to distribute mass of "dontknowup"
  mutate(mean_positive = (1.5*inf_1to2 + 3.5*inf_3to4 + 5*inf_5 + 7.5*inf_6to9 + 12*inf_10to14)/(inf_1to2 + inf_3to4 + inf_5 + inf_6to9 + inf_10to14)) %>%
  # for "those who "dontknow" use distribution as prior
  # i.e. ignore them
  pivot_longer(cols = starts_with("inf"),
               names_to = "x",
               values_to = "freq") %>%
  mutate(x = as.numeric(recode(x,
                    inf_lower = "-5",
                    inf_downorsame = "-2",
                    inf_1to2 = "1.5",
                    inf_3to4 = "3.5",
                    inf_5 = "5",
                    inf_6to9 = "7.5",
                    inf_10to14 = "12",
                    inf_higher = "17.5",
                    inf_dontknowup = as.character(mean_positive))),
         year = as.factor(year)) %>%
  select(-mean_positive) %>%
  group_by(year, x) %>%
  summarise(frequency = sum(freq),
            .groups = "drop") %>%
  group_by(year) %>%
  mutate(sum = sum(frequency),
         y = frequency/sum) %>%
  select(-c(sum, frequency))

plot <- ggplot(dtf_michigan,
               aes(x = x, y = y, col = year)) +
          stat_density(aes(x = x, weight = y,
                           colour = year, linetype = year),
                       inherit.aes = FALSE,
                       geom = "line",
                       position = "identity",
                       size = line_size,
                       bw = 1.3) +
          scale_x_discrete(limits = c(1.5, 3.5, 5, 7.5, 12)) +
          scale_colour_manual(breaks = breaks, values = legv, labels = legs) +
          scale_linetype_manual(breaks = breaks, values = linev, labels = legs) +
          theme(legend.position = c(0.1, 0.7)) +
          guides(col = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_6_b.pdf"), plot)

```

## Figure(s) 7: Media mentions and public concerns related to inflation

```{r fig_7}

# variables left axis
vars1 <- c("proquest_central_bank",
           "proquest_inflation")
# variables right axis
vars2 <- c("gallup_inflation_minus_employment")
legv <- create_legv(c(vars1, vars2))
linev <- create_linev(c(vars1, vars2))
legs <- c("Central bank % mentions in NYT (left axis)",
          "Inflation % mentions in NYT (left axis)",
          "Concern about inflation - about unemployment Gallup (right axis)")

transf <- 5

dtf_mentions <- dtf_long %>%
                 filter(date >= as.Date("1965-01-01") &
                        date <= as.Date("1974-12-31") &
                        variable %in% c(vars1, vars2) &
                        !is.na(value)) %>% 
                 mutate(variable = factor(variable,levels = c(vars1, vars2)))

plot <- ggplot(dtf_mentions %>%
                 filter(variable %in% c(vars1)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable),
                    size = line_size) +
          geom_line(data = dtf_mentions %>%
                      filter(variable %in% c(vars2)),
                    aes(y = value / transf,
                        colour = variable, linetype = variable,
                        order = as.numeric(variable)), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                             sec.axis = sec_axis(trans = ~ . * transf,
                                                 labels = scales::percent_format(accuracy = 1))
                            ) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y")  +
          scale_linetype_manual(breaks = c(vars1, vars2),
                                values = linev, labels = legs) +  
          scale_colour_manual(breaks = c(vars1, vars2), values = legv, labels = legs) +
          theme(legend.position = c(0.36, 0.85)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))


print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_7.pdf"), plot)


```

## Figure(s) 8: Firm expectations of inflation

```{r fig_8}

vars <- unique(dtf_long$variable[grepl("leew_", dtf_long$variable) &
                                 grepl("exp", dtf_long$variable)]
               )

legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("% change in prices of goods and services sold",
          "% change in prices of capital goods purchased")

plot <- ggplot(dtf_long %>% 
                filter(variable %in% vars &
                       !is.na(value) &
                       date <= as.Date("1975-01-01")) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          theme(legend.position = c(0.4,0.85)) +
          scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_8.pdf"), plot)

```

## Figure(s) 9: Gold prices, spot, per ounce

```{r fig_9}

#plot
vars <- c("gold",
          "gold_price")
legv <- c("red","blue","black")
linev <- c("solid","longdash","dashed")
legs <- c("Zurich price",
          "London price",
          "USD to gold ounce convertability")

#USD GOLD PEG
peg <- 35

#position of segment
dtf_peg <- data.frame(x1 = as.Date("1966-03-30"),
                      x2 = as.Date("1971-08-13"),
                      y1 = peg, y2 = peg,
                      leg = "USD gold convertability")

plot <- ggplot(dtf_long %>%
                 filter(date >= as.Date("1966-03-30") &
                        date <= as.Date("1971-06-30") &
                        variable %in% vars &
                        !is.na(value)),
               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") +
          geom_segment(aes(x = x1, xend = x2, y = y1, yend = y2,
                           colour = leg, linetype = leg),
                       data = dtf_peg, size = 2) +
          scale_colour_manual(values = legv, labels = legs) +
          scale_linetype_manual(values = linev, labels = legs)  +
          theme(legend.position = c(0.2,0.9)) +
          guides(colour = guide_legend(keywidth = 5, keyheight = 1))
          
print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_9.pdf"), plot)
rm(dtf_peg)

```

## Figure(s) 10 - 13: MATLAB or other countries

## Figure(s) 14: Dropping the anchor: the US 1980s
## Subfigure 14(a): Actual and survey first-order moments

```{r fig_14_a}

dtf_MRW <- dtf
# clean original michigan values
input <- read_dta("./Data/US/MRWdata_0.dta")
input %<>% mutate(year = floor(date/100),
                  month = date %% 100,
                  quarter = lubridate::quarter(month)) %>%
  group_by(year, quarter) %>%
  summarise(mi_picpi12_mean_original = mean(mi_picpi12_mean),
            mi_picpi12_median_original = mean(mi_picpi12_median),
            .groups = "drop") %>%
  mutate(date = zoo::as.Date(zoo::as.yearqtr(year + ((quarter - 1)/4))))
  
# join
dtf_MRW %<>% left_join(input, by = "date") %>%
  select(-c(mi_picpi12_mean, mi_picpi12_median)) %>%
  rename(mi_picpi12_mean = mi_picpi12_mean_original,
         mi_picpi12_median = mi_picpi12_median_original) %>%
  select(date, spf_pigdp12_median, mi_picpi12_median)

# pivot
dtf_MRW %<>% pivot_longer(cols = !date,
                         names_to = "variable",
                         values_to = "value")
dtf_temp <- dtf_macro %>%
  select(date, CPIAUCSL, CPILFESL) %>%
  arrange(date) %>%
  mutate(inflation_CoreCPI = (CPIAUCSL - lag(CPIAUCSL, n = 4))/lag(CPIAUCSL, n = 4),
         inflation_CPI = (CPILFESL - lag(CPILFESL, n = 4))/lag(CPILFESL, n = 4)) %>%
  pivot_longer(cols = -date,
               names_to = "variable",
               values_to = "value")

dtf_dropping <- full_join(dtf_MRW, dtf_temp, by = c("date", "variable", "value")) %>%
  arrange(date, variable) %>%
  mutate(date = as.Date(date))

vars <- c("spf_pigdp12_median",
          "mi_picpi12_median",
          "inflation_CPI",
          "inflation_CoreCPI")
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("SPF median",
          "Michigan median",
          "Inflation (CPI)",
          "Inflation (Core CPI)")

plot <- ggplot(dtf_dropping %>%
                 filter(date >= as.Date("1978-01-01") &
                        date <= as.Date("1984-12-30") &
                        variable %in% vars &
                        !is.na(value)) %>%
                 mutate(variable = factor(variable, levels = vars)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable, linetype = variable), size = line_size) +
          scale_y_continuous(labels = scales::percent_format(accuracy = 0.1)) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values= linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          theme(legend.position = c(0.2,0.2)) +
          guides(linetype = guide_legend(keywidth = 5, keyheight = 1))


print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_14_a.pdf"), plot)
rm(dtf_MRW, dtf_temp)

```

## Subfigure 14(b): Survey disagreement

```{r fig_14_b}

# 1979 - 1985: Disagreement, Skewness for michigan; based on MRW data

# disagreement: standard deviation
# skewness: build from percentiles

vars_right <- c("mi_picpi12_sd")
vars_left <- c("mi_picpi12_skewness")
vars <- c(vars_right, vars_left)
legv <- create_legv(vars)
linev <- create_linev(vars)
legs <- c("Standard deviation (right axis)",
          "Skewness (left axis)")

transf <- 20

dtf_temp <- dtf_long %>%
  filter(date >= as.Date("1979-01-01") &
         date <= as.Date("1984-12-30") &
         variable %in% vars &
         !is.na(value)) %>%
  mutate(variable = factor(variable, levels = vars))

plot <- ggplot(dtf_temp %>%
                 filter(variable %in% c(vars_left)),
               aes(x = date, y = value)) +
          geom_line(aes(colour = variable,
                        linetype = variable),
                    size = line_size) +
          geom_line(data = dtf_temp %>%
                      filter(variable %in% c(vars_right)),
                    aes(x = date, y = transf * value,
                        colour = variable, linetype = variable,
                        order = as.numeric(variable)),
                    size = line_size) +
          scale_y_continuous(sec.axis = sec_axis(trans = ~ (. / transf),
                             labels = scales::percent_format(accuracy = 1))
                            ) +
          scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
          scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
          scale_colour_manual(breaks = vars, values = legv, labels = legs) +
          theme(legend.position = c(0.4,0.9)) +
          guides(linetype = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_14_b.pdf"), plot)
rm(dtf_temp)

```

## Figure(s) 15: The expected inflation anchor through the pandemic
## Subfigure 15(a): Actual inflation

```{r fig_15_a}

time_series_files <- list.files("./Data/US/US_macrodata", pattern = "*.xls", full.names = TRUE)
dtf_inflation <- as.data.frame(seq.Date(from = as.Date("2016-01-01"),
                                           to = as.Date("2021-07-01"),
                                           by = "month"))
colnames(dtf_inflation) <- "date"

# clean and merge all files in the macrodata-directory
for (filename in time_series_files) {
  if (grepl("USREC", filename)) next
    skip <- 10
    input <- read_xls(filename, skip = skip)
    type_freq <- colnames(read_xls(filename, range = "A10"))
    type_freq <- tolower(gsub("Frequency: ", "", type_freq))
    
    value_name <- gsub(".xls", "", gsub("./Data/US/US_macrodata/", "", filename))
    colnames(input) <- c("date", "value")
    # determine frequency, take means over quarter if necessary
    
    if (type_freq %in% c("monthly")) { # monthly
      input %<>% rename(!!value_name := value)
      dtf_inflation %<>% left_join(input, by = "date")
    }
}

dtf_inflation %<>%
  mutate(date = as.Date(date),
         CPIAUCSL = 100*( (CPIAUCSL - lag(CPIAUCSL, 12))/lag(CPIAUCSL, 12)),
         CPILFESL = 100*( (CPILFESL - lag(CPILFESL, 12))/lag(CPILFESL, 12)))

dtf_inflation %<>% pivot_longer(cols = -any_of("date"),
                                names_to = "variable",
                                values_to = "value")

vars <- c("CPILFESL", "CPIAUCSL", "PCETRIM12M159SFRBDAL")
linev <- c(create_linev(vars), "solid")
legv <- c(create_legv(vars), "black")
legs <- c("CPI Urban Core", "CPI Urban All Goods", "Trimmed Mean PCE", "Inflation Target")

plot <- ggplot(dtf_inflation %>%
                   filter(variable %in% vars,
                          date >= as.Date("2018-01-01"),
                          date <= as.Date("2021-07-01")) %>%
                   mutate(variable = as.factor(variable)),
                 aes(x = date, y = value)) +
  geom_hline(aes(col = "Inflation Target",
                 linetype = "Inflation Target"),
             yintercept = 2, size = line_size) +
  geom_line(aes(col = variable, linetype = variable),
            size = line_size) +
  scale_y_continuous(labels = function(x) paste(x, "%"), limits = c(-1, 6), breaks = seq(0, 6, by = 1)) +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  scale_linetype_manual(values = linev, labels = legs) +  
  scale_colour_manual(values = legv, labels = legs) +
  theme(legend.position = c(0.5,0.9)) +
  guides(linetype = guide_legend(keywidth = 7, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_15_a.pdf"), plot)

```

## Subfigure 15(b): Markets and survey first-order moments

```{r fig_15_b}

dtf_households <- read_xlsx("./Data/US/2_Michigan.xlsx", sheet = "Michigan", skip = 2)
dtf_households %<>% rename(date = Date) %>%
  mutate(date = as.Date(date),
         year = year(date),
         quarter = quarter(date)) %>%
  group_by(year, quarter) %>%
  summarise(date = first(date),
            across(starts_with("pie_michigan"), mean),
            .groups = "drop") %>%
  # skew is Pearson's 2nd nonparametric skew coefficient
  mutate(pie_michigan_sd_1y = 3*(pie_michigan_mean_1y -pie_michigan_median_1y) / pie_michigan_skew_1y,
         pie_michigan_sd_5y = 3*(pie_michigan_mean_5y -pie_michigan_median_5y) / pie_michigan_skew_5y)

# read and clean SPF one-year ahead forecast
dtf_nonhouseholds <- read_xlsx("./Data/US/Individual_CPI.xlsx")
dtf_nonhouseholds %<>%
  rename_with(tolower) %>%
  rename(pie_people_1y = cpi6) %>%
  mutate(date = year + ((quarter - 1)/4),
         date = zoo::as.Date(zoo::as.yearqtr(date))) %>%
  select(date, year, quarter, id, industry, pie_people_1y) %>%
  filter(date >= as.Date("2018-01-01")) %>%
  mutate(pie_people_1y = ifelse(pie_people_1y == "#N/A", NA, pie_people_1y),
         pie_people_1y = as.numeric(pie_people_1y),
         industry = as.factor(industry))
levels(dtf_nonhouseholds$industry) <- c("fin", "bus", "unknown")

dtf_nonhouseholds_all <- dtf_nonhouseholds %>%
  group_by(year, quarter) %>%
  summarise(date = first(date),
            pie_people_1y_all_mean = mean(pie_people_1y, na.rm = TRUE),
            pie_people_1y_all_sd = sd(pie_people_1y, na.rm = TRUE),
            pie_people_1y_all_median = median(pie_people_1y, na.rm = TRUE),
            pie_people_1y_all_interq = IQR(pie_people_1y, na.rm = TRUE),
            pie_people_1y_all_skew = moments::skewness(pie_people_1y, na.rm = TRUE),
            .groups = "drop") %>%
  select(-c(year, quarter))

dtf_nonhouseholds_fin <- dtf_nonhouseholds %>%
  filter(as.integer(industry) == 1) %>%
  group_by(year, quarter) %>%
  summarise(date = first(date),
            pie_people_1y_fin_mean = mean(pie_people_1y, na.rm = TRUE),
            pie_people_1y_fin_sd = sd(pie_people_1y, na.rm = TRUE),
            pie_people_1y_fin_median = median(pie_people_1y, na.rm = TRUE),
            pie_people_1y_fin_interq = IQR(pie_people_1y, na.rm = TRUE),
            pie_people_1y_fin_skew = moments::skewness(pie_people_1y, na.rm = TRUE),
            .groups = "drop") %>%
  select(-c(year, quarter))

dtf_nonhouseholds_bus <- dtf_nonhouseholds %>%
  filter(as.integer(industry) == 2) %>%
  group_by(year, quarter) %>%
  summarise(date = first(date),
            pie_people_1y_bus_mean = mean(pie_people_1y, na.rm = TRUE),
            pie_people_1y_bus_sd = sd(pie_people_1y, na.rm = TRUE),
            pie_people_1y_bus_median = median(pie_people_1y, na.rm = TRUE),
            pie_people_1y_bus_interq = IQR(pie_people_1y, na.rm = TRUE),
            pie_people_1y_bus_skew = moments::skewness(pie_people_1y, na.rm = TRUE),
            .groups = "drop") %>%
  select(-c(year, quarter))
  
dtf_nonhouseholds_all %<>%
  left_join(dtf_nonhouseholds_fin, by = "date") %>%
  left_join(dtf_nonhouseholds_bus, by = "date")

# join with Household Survey (Michigan survey of Consumers)
dtf_modern <- left_join(dtf_nonhouseholds_all, dtf_households, by = "date")
dtf_modern %<>% left_join(dtf_macro %>% select(date, T10YIE), by = "date")

dtf_modern %<>%
  pivot_longer(cols = -date,
               names_to = "variable",
               values_to = "value")

vars <- c("pie_michigan_mean_1y", "pie_michigan_mean_5y", "pie_people_1y_all_mean", "T10YIE")
linev <- create_linev(vars)
legv <- create_legv(vars)
legs <- c("People: Michigan 1 Year", "People: Michigan 5 Years", "Traders: SPF 1 Year", "Market: 10 year Breakeven Inflation Rate")

plot <- ggplot(dtf_modern %>%
                 filter(variable %in% vars) %>%
                 mutate(variable = as.factor(variable)),
               aes(x = date, y = value)) +
  geom_line(aes(col = variable, linetype = variable),
            size = line_size) +
  scale_y_continuous(labels = function(x) paste(x, "%")) +
  scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
  scale_colour_manual(breaks = vars, values = legv, labels = legs) +
  theme(legend.position = c(0.5,0.7)) +
  guides(linetype = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_15_b.pdf"), plot)

```

# Subfigure 15(c): Cross-sectional disagreement of households

```{r fig_15_c}

vars_left <- c("pie_michigan_skew_1y")
vars_right <- c("pie_michigan_sd_1y")
vars <- c(vars_right, vars_left)
legv <- c('red', 'blue')
linev <- c('solid', 'longdash')
legs <- c("Standard deviation (right axis)", "Skewness (left axis)")

plot <- ggplot(dtf_modern %>%
                 filter(variable %in% vars_left) %>%
                 mutate(variable = as.factor(variable)),
               aes(x = date, y = value)) +
  geom_line(aes(col = variable, linetype = variable),
            size = line_size) +
  geom_line(data = dtf_modern %>%
              filter(variable %in% vars_right) %>%
              mutate(variable = as.factor(variable)),
            aes(x = date, y = value / 4, col = variable, linetype = variable),
            size = line_size) +
  scale_y_continuous(sec.axis = sec_axis(trans = ~ . * 4)) +
  scale_linetype_manual(breaks = vars, values = linev, labels = legs) +  
  scale_colour_manual(breaks = vars, values = legv, labels = legs) +
  theme(legend.position = c(0.3,0.8)) +
  guides(linetype = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_15_c.pdf"), plot)

```

# Plot 4: Distribution of Household Expectations across different months (not quarters, unlike A3!)

```{r fig_15_d}

# Build monthly histogram of Household Inflation Expectation
dtf_histogram <- read_xlsx("./Data/US/2_Michigan.xlsx", sheet = "Next year", skip = 2)

dtf_histogram %<>%
  rename_with(tolower) %>%
  select(year, month, down, same, starts_with("up"), starts_with("dk")) %>%
  mutate(inf_lowerbound = 0, # fix tails
         inf_upperbound = 0) %>%
  rename(inf_down = down,
         inf_0 = same,
         inf_1to2 = `up by 1-2%`,
         inf_3to4 = `up by 3-4%`,
         inf_5 = `up by 5%`,
         inf_6to9 = `up by 6-9%`,
         inf_10to14 = `up by 10-14%`,
         inf_15plus = `up by 15%+`,
         inf_dontknowup = `up; dk how much`,
         inf_dontknow = `dk; na`
         ) %>%
  mutate(mean_positive = (1.5*inf_1to2 + 3.5*inf_3to4 + 5*inf_5 + 7.5*inf_6to9 + 12*inf_10to14 + 17*inf_15plus)/(inf_1to2 + inf_3to4 + inf_5 + inf_6to9 + inf_10to14 + inf_15plus)) %>% # deal with "dontknowup"
  select(-inf_dontknow) %>% # use common distribution as prior for "dontknow"
  pivot_longer(cols = starts_with("inf"),
               names_to = "x",
               values_to = "freq") %>%
  mutate(x = as.numeric(recode(x,
                    inf_lowerbound = "-5",
                    inf_down = "-2",
                    inf_0 = "0",
                    inf_1to2 = "1.5",
                    inf_3to4 = "3.5",
                    inf_5 = "5",
                    inf_6to9 = "7.5",
                    inf_10to14 = "12",
                    inf_15plus = "17",
                    inf_upperbound = "20",
                    inf_dontknowup = as.character(mean_positive)))) %>%
  select(-mean_positive) %>%
  # turn frequencies into probabilities
  group_by(year, month) %>%
  mutate(sum = sum(freq),
         y = freq /sum) %>%
  select(-c(sum, freq)) %>%
  mutate(date = year + ((month - 1)/12),
         date = zoo::as.Date(zoo::yearmon(date)),
         group = 0,
         group = ifelse(year == 2020 & month == 1, 1, group),
         group = ifelse(year == 2020 & month == 9, 2, group),
         group = ifelse(year == 2021 & month == 6, 3, group))

linev <- c("solid", "dashed", "dotted")
legv <- c('red', 'blue', 'green4')
legs <- c("2020 January", "2020 September", "2021 June")
breaks = c(1, 2, 3)

plot <- ggplot(dtf_histogram %>%
                 filter(group %in% breaks) %>%
                 mutate(group = as.factor(group)),
               aes(x = x, y = y, col = group)) +
          stat_density(aes(x = x, weight = y,
                           colour = group, linetype = group),
                       inherit.aes = FALSE,
                       geom = "line",
                       position = "identity",
                       size = line_size,
                       bw = 1.3) +
          scale_x_discrete(limits = c(0, 1.5, 3.5, 5, 7.5, 12)) +
          scale_colour_manual(breaks = breaks, values = legv, labels = legs) +
          scale_linetype_manual(breaks = breaks, values = linev, labels = legs) +
          theme(legend.position = c(0.6, 0.7)) +
          guides(col = guide_legend(keywidth = 5, keyheight = 1))

print(plot)
if (bSavePlots) my_ggsave(paste0(path_figure_out, "figure_15_d.pdf"), plot)

```
