Code
# Load packages
library(lpirfs)
library(dplyr)
library(gridExtra)
library(ggpubr)
library(readxl)
library(vars)
library(ggplot2)
library(zoo)
#####################################################################################################
# --- Code for Figure 1 ---
#####################################################################################################
# Load data from lpirfs package
endog_data <- interest_rules_var_data
# Estimate linear model with lpirfs function
results_lin <- lp_lin(endog_data,
lags_endog_lin = 4,
trend = 0,
shock_type = 0,
confint = 1.96,
hor = 12)
# Show summary. Equals table 3 in the paper
summary(results_lin)[[1]][1]
## [[1]]
## R-sqrd. Adj. R-sqrd. F-stat p-value
## h1:GDP_gap 0.9092137 0.9030238 146.8850 6.566021e-85
## h1:Infl 0.8403042 0.8294158 77.1746 1.707770e-63
## h1:FF 0.9371636 0.9328793 218.7439 6.592407e-99
# Figure 1 in paper
plot(results_lin)
#####################################################################################################
# --- Code for Figure 2 ---
#####################################################################################################
# Choose data for switching variable (here federal funds rate)
switching_data <- if_else(dplyr::lag(endog_data$Infl, 3) > 4.75, 1, 0)
# Estimate model and save results
results_nl <- lp_nl(endog_data,
lags_endog_lin = 4,
lags_endog_nl = 4,
trend = 0,
shock_type = 0,
confint = 1.96,
hor = 12,
switching = switching_data,
lag_switching = FALSE,
use_logistic = FALSE)
# Use plot functions
nl_plots <- plot_nl(results_nl)
# Combine plots by using 'ggpubr' and 'gridExtra'
single_plots <- nl_plots$gg_s1[c(3, 6, 9)]
single_plots[4:6] <- nl_plots$gg_s2[c(3, 6, 9)]
all_plots <- sapply(single_plots, ggplotGrob)
# Show all plots
nl_all_plots <- marrangeGrob(all_plots, nrow = 3, ncol = 2, top = NULL)
nl_all_plots
#####################################################################################################
# --- Code for Figure 3 ---
#####################################################################################################
# Load data
ag_data <- ag_data
sample_start <- 7
sample_end <- dim(ag_data)[1]
# Endogenous data
endog_data <- ag_data[sample_start:sample_end,3:5]
# Variable to shock with. Here government spending due to
# Blanchard and Perotti (2002) framework
shock <- ag_data[sample_start:sample_end, 3]
# Estimate linear model
results_lin_iv <- lp_lin_iv(endog_data,
lags_endog_lin = 4,
shock = shock,
trend = 0,
confint = 1.96,
hor = 20)
# Make and save plots
iv_lin_plots <- plot_lin(results_lin_iv)
# This example replicates results from the Supplementary Appendix
# by Ramey and Zubairy (2018) (RZ-18).
# Load and prepare data
ag_data <- ag_data
endog_data <- ag_data[sample_start:sample_end, 3:5]
# The nonlinear shock is estimated by RZ-18.
shock <- ag_data[sample_start:sample_end, 7]
# Include four lags of the 7-quarter moving average growth rate of GDP
# as exogenous variables (see RZ-18)
exog_data <- ag_data[sample_start:sample_end, 6]
# Use the 7-quarter moving average growth rate of GDP as switching variable
# and adjust it to have suffiently long recession periods.
switching_variable <- ag_data$GDP_MA[sample_start:sample_end] - 0.8
# Estimate local projections
results_nl_iv <- lp_nl_iv(endog_data,
lags_endog_nl = 3,
shock = shock,
exog_data = exog_data,
lags_exog = 4,
trend = 0,
confint = 1.96,
hor = 20,
use_hp = FALSE,
switching = switching_variable,
gamma = 3)
# Make and save plots
plots_nl_iv <- plot_nl(results_nl_iv)
# Make to list to save all plots
combine_plots <- list()
# Save linear plots in list
combine_plots[[1]] <- iv_lin_plots[[1]]
combine_plots[[2]] <- iv_lin_plots[[3]]
# Save nonlinear plots for expansion period
combine_plots[[3]] <- plots_nl_iv$gg_s1[[1]]
combine_plots[[4]] <- plots_nl_iv$gg_s1[[3]]
# Save nonlinear plots for recession period
combine_plots[[5]] <- plots_nl_iv$gg_s2[[1]]
combine_plots[[6]] <- plots_nl_iv$gg_s2[[3]]
lin_plots_all <- sapply(combine_plots, ggplotGrob)
combine_plots_all <- marrangeGrob(lin_plots_all, nrow = 2, ncol = 3, top = NULL)
# Show all plots
combine_plots_all
#####################################################################################################
# --- Code for Figure 4 ---
#####################################################################################################
# Go to the website of the 'The MacroFinance and MacroHistory Lab'
# Download the Excel-Sheet of the 'Jordà-Schularick-Taylor Macrohistory Database':
# URL: https://www.macrohistory.net/database/
# Then uncomment and run the code below...
# # Load data set
# jst_data <- read_excel("JSTdatasetR5.xlsx", sheet = "Data")
#
#
# # Swap the first two columns
# jst_data <- jst_data %>%
# dplyr::filter(year <= 2013) %>%
# dplyr::select(country, year, everything())
#
# # Prepare variables
# data_set <- jst_data %>%
# mutate(stir = stir) %>%
# mutate(mortgdp = 100*(tmort/gdp)) %>%
# mutate(hpreal = hpnom/cpi) %>%
# group_by(country) %>%
# mutate(hpreal = hpreal/hpreal[year==1990][1]) %>%
# mutate(lhpreal = log(hpreal)) %>%
#
# mutate(lhpy = lhpreal - log(rgdppc)) %>%
# mutate(lhpy = lhpy - lhpy[year == 1990][1]) %>%
# mutate(lhpreal = 100*lhpreal) %>%
# mutate(lhpy = 100*lhpy) %>%
# ungroup() %>%
#
# mutate(lrgdp = 100*log(rgdppc)) %>%
# mutate(lcpi = 100*log(cpi)) %>%
# mutate(lriy = 100*log(iy*rgdppc)) %>%
# mutate(cay = 100*(ca/gdp)) %>%
# mutate(tnmort = tloans - tmort) %>%
# mutate(nmortgdp = 100*(tnmort/gdp)) %>%
# dplyr::select(country, year, mortgdp, stir, ltrate,
# lhpy, lrgdp, lcpi, lriy, cay, nmortgdp)
#
#
# # Exclude observations from WWI and WWII
# data_sample <- seq(1870, 2016)[which(!(seq(1870, 2016) %in%
# c(seq(1914, 1918),
# seq(1939, 1947))))]
#
# # Estimate linear panel model
# results_panel <- lp_lin_panel(data_set = data_set, data_sample = data_sample,
# endog_data = "mortgdp", cumul_mult = TRUE,
# shock = "stir", diff_shock = TRUE,
# panel_model = "within", panel_effect = "individual",
# robust_cov = "vcovSCC", c_exog_data = "cay",
# c_fd_exog_data = colnames(data_set)[c(seq(4,9),11)],
# l_fd_exog_data = colnames(data_set)[c(seq(3,9),11)],
# lags_fd_exog_data = 2, confint = 1.67,
# hor = 10)
#
#
# # Plot irfs
# plot(results_panel)
#####################################################################################################
# --- Code for Figure 5 ---
#####################################################################################################
# # Estimate panel model
# results_panel <- lp_nl_panel(data_set = data_set,
# data_sample = data_sample,
# endog_data = "mortgdp", cumul_mult = TRUE,
# shock = "stir", diff_shock = TRUE,
# panel_model = "within", panel_effect = "individual",
# robust_cov = "vcovSCC", switching = "lrgdp",
# lag_switching = TRUE, use_hp = TRUE,
# lambda = 6.25, gamma = 10,
# c_exog_data = "cay",
# c_fd_exog_data = colnames(data_set)[c(seq(4,9),11)],
# l_fd_exog_data = colnames(data_set)[c(seq(3,9),11)],
# lags_fd_exog_data = 2,
# confint = 1.67,
# hor = 10)
#
#
#
# # Show non-linear plots
# plot(results_panel)
#####################################################################################################
# --- Code for Figure 6 ---
#####################################################################################################
# Load data from lpirfs package
endog_data <- interest_rules_var_data
hor <- 12
p_lags <- c(2, 4, 6)
# Results for lpirfs
results_irf_lpirfs_mean <- array(NA, c(dim(endog_data)[2], hor + 1, 3))
results_irf_lpirfs_low <- results_irf_lpirfs_mean
results_irf_lpirfs_up <- results_irf_lpirfs_mean
# Results for SVARS
results_irf_svar_mean <- array(NA, c(dim(endog_data)[2], hor + 1, 3))
results_irf_svar_low <- results_irf_svar_mean
results_irf_svar_up <- results_irf_svar_mean
# Estimate irfs for Jordá method
for(ii in seq_along(p_lags)){
results_lin <- lp_lin(endog_data,
lags_endog_lin = p_lags[ii],
trend = 0,
shock_type = 0,
confint = 1.96,
hor = 12)
results_irf_lpirfs_mean[, , ii] <- results_lin$irf_lin_mean[, , 1]
results_irf_lpirfs_low[, , ii] <- results_lin$irf_lin_low[, , 1]
results_irf_lpirfs_up[, , ii] <- results_lin$irf_lin_up[, , 1]
}
amat <- diag(3)
diag(amat) <- NA
# Estimate results for SVARS
for(ii in seq_along(p_lags)){
# Estimate VAR
var_results <- VAR(endog_data, p = p_lags[ii], type = "const")
## Estimation method scoring
svar_endog_data <- SVAR(x = var_results, estmethod = "scoring", Amat = amat, Bmat = NULL,
max.iter = 100, maxls = 1000, conv.crit = 1.0e-8)
results_irf_svar <- irf(svar_endog_data, impulse = colnames(endog_data), n.ahead = hor)
results_irf_svar_mean[, , ii] <- t(results_irf_svar$irf[[1]])
results_irf_svar_low[, , ii] <- t(results_irf_svar$Lower[[1]])
results_irf_svar_up[, , ii] <- t(results_irf_svar$Upper[[1]])
}
shock_names <- names(endog_data)
plot_num <- 1
gg_lin <- rep(list(NaN), 3)
x_labs <- c("p = 2", "p = 4", "p = 6")
gg_lin <- list()
second_color <- "#D55E00"
# Loop to fill to create plots
plot_num <- 1
for (kk in seq_along(p_lags)){
for (rr in seq_along(p_lags)){
legend_title <- paste("p = ", p_lags[kk], sep = "")
# Extract relevant impulse responses
tbl_lpirfs_mean <- as.matrix(t(results_irf_lpirfs_mean[, 1:hor , kk]))[, rr]
tbl_lpirfs_low <- as.matrix(t(results_irf_lpirfs_low[, 1:hor , kk]))[, rr]
tbl_lpirfs_up <- as.matrix(t(results_irf_lpirfs_up[, 1:hor , kk]))[, rr]
tbl_svar_mean <- as.matrix(t(results_irf_svar_mean[, 1:hor , kk]))[, rr]
tbl_svar_low <- as.matrix(t(results_irf_svar_low[, 1:hor , kk]))[, rr]
tbl_svar_up <- as.matrix(t(results_irf_svar_up[, 1:hor , kk]))[, rr]
# Convert to tibble for ggplot
tbl_lin_lpirfs <- tibble(x = seq_along(tbl_lpirfs_mean), mean = tbl_lpirfs_mean,
low = tbl_lpirfs_low, up = tbl_lpirfs_up)
tbl_lin_svar <- tibble(x = seq_along(tbl_svar_mean), mean = tbl_svar_mean,
low = tbl_svar_low, up = tbl_svar_up)
gg_lin[[plot_num]] <- ggplot()+
geom_line(data = tbl_lin_lpirfs, aes(y = mean, x = x, linetype = "a", color = "a")) +
geom_ribbon(data = tbl_lin_lpirfs, aes(x = x, ymin = low, ymax = up), col = 'grey',
fill = 'grey', alpha = 0.3) +
geom_line(data = tbl_lin_svar, aes(y = mean, x = x, linetype = "b", color ="b")) +
geom_ribbon(data = tbl_lin_svar, aes(x = x, ymin = low, ymax = up), col = second_color,
linetype = "dashed",
fill = second_color, alpha = 0.1) +
scale_linetype_manual(name = x_labs[kk],
values = c(1, 5),
labels = c("lpirfs", "vars"),
guide = guide_legend(title.position="top", title.hjust = 0.5)) +
scale_color_manual(name = x_labs[kk],
labels = c("lpirfs", "vars"),
values = c("a" = "black", "b" = second_color),
guide = guide_legend(title.position="top", title.hjust = 0.5)) +
theme_classic() +
ggtitle(paste( shock_names[1], 'on', shock_names[rr], sep=" ")) +
xlab('') +
ylab('') +
theme(title = element_text(size = 7),
plot.title = element_text(hjust = 0.5),
axis.text = element_text(size = 8),
legend.position = "bottom",
legend.margin = margin(t = -.25, r = 0, b = 0, l = .75, unit = "cm"),
# legend.title = element_text(size = 8),
legend.title = element_blank(),
legend.spacing.y = unit(-.25, 'cm'),
legend.spacing.x = unit(.05, 'cm'),
legend.text = element_text(size = 7),
legend.box = "horizontal") +
scale_y_continuous(expand = c(0, 0)) +
scale_x_continuous(expand = c(0, 0),
breaks = seq(0, hor, 2))
if(rr == 1) gg_lin[[plot_num]] <- gg_lin[[plot_num]] + ylim(-.5, 1.2)
if(rr == 2) gg_lin[[plot_num]] <- gg_lin[[plot_num]] + ylim(-.2, 1)
if(rr == 3) gg_lin[[plot_num]] <- gg_lin[[plot_num]] + ylim(-.3, 1.4)
# Add one to count variable
plot_num <- plot_num + 1
}
}
# Make column plots
a_1 <- ggarrange(gg_lin[[1]], gg_lin[[2]], gg_lin[[3]], ncol = 1, nrow = 3, common.legend = TRUE, legend = "bottom")
a_1 <- annotate_figure(a_1, bottom = text_grob(paste("a.)", "p =", p_lags[1], sep = " "), size = 7, hjust = -.1, vjust = 0.5))
a_2 <- ggarrange(gg_lin[[4]], gg_lin[[5]], gg_lin[[6]], ncol = 1, nrow = 3, common.legend = TRUE, legend = "bottom")
a_2 <- annotate_figure(a_2, bottom = text_grob(paste("b.)", "p = ", p_lags[2], sep = " "), size = 7, hjust = -.1, vjust = 0.5))
a_3 <- ggarrange(gg_lin[[7]], gg_lin[[8]], gg_lin[[9]], ncol = 1, nrow = 3, common.legend = TRUE, legend = "bottom")
a_3 <- annotate_figure(a_3, bottom = text_grob(paste("c.)", "p = ", p_lags[3], sep = " "), size = 7, hjust = -.1, vjust = 0.5))
# Combine columns
combine_plot <- ggarrange(a_1, a_2, a_3, ncol = 3)
combine_plot
#####################################################################################################
# --- Code for Figure 7 ---
#####################################################################################################
# Recession dates
start_rec <- c("1957 Q3", "1960 Q2", "1970 Q1", "1973 Q4", "1980 Q1", "1981 Q3", "1990 Q3", "2001 Q2")
end_rec <- c("1958 Q2", "1961 Q1", "1970 Q4", "1975 Q1", "1980 Q3", "1982 Q4", "1991 Q1", "2001 Q4")
# Quarterly fequence for Jordá data
dates <- as.yearqtr(seq(as.Date("1955/12/1"), as.Date("2003/1/1"), by = "quarter"))
nber_rec_se <- tibble(start = as.yearqtr(start_rec), end = as.yearqtr(end_rec)) %>%
filter(start %in% dates) %>%
mutate(start = as.Date(start)) %>%
mutate(end = as.Date(end))
# Convert back with as.Date for ggplot
dates <- as.Date(dates)
# Load data from lpirfs package
endog_data <- interest_rules_var_data
switching_variable <- interest_rules_var_data$GDP_gap
hor <- 12
shock_pos <- 3
# Results for lpirfs
results_s1_mean <- matrix(NA, 3, hor + 1)
results_s1_low <- results_s1_mean
results_s1_up <- results_s1_mean
results_s2_mean <- matrix(NA, 3, hor + 1)
results_s2_low <- results_s2_mean
results_s2_up <- results_s2_mean
fz_mat <- matrix(NA, 3, dim(endog_data)[1] - 4)
# Choose values for lambda and gamma
gamma_vals <- c(1, 5, 10)
#lambda_vals <- c(6.25, 1600, 129,600)
for(ii in seq_along(gamma_vals)){
# Estimate linear model with lpirfs function
results_nl <- lp_nl(endog_data,
lags_endog_lin = 4,
lags_endog_nl = 4,
trend = 0,
shock_type = 0,
switching = switching_variable,
use_hp = TRUE,
lambda = 1600,
gamma = gamma_vals[ii],
confint = 1.96,
hor = 12,
num_cores = 1)
results_s1_mean[ii, ] <- results_nl$irf_s1_mean[1, , 3]
results_s1_low[ii, ] <- results_nl$irf_s1_low[1, , 3]
results_s1_up[ii, ] <- results_nl$irf_s1_up[1, , 3]
results_s2_mean[ii, ] <- results_nl$irf_s2_mean[1, , 3]
results_s2_low[ii, ] <- results_nl$irf_s2_low[1, , 3]
results_s2_up[ii, ] <- results_nl$irf_s2_up[1, , 3]
fz_mat[ii, ] <- results_nl$fz
}
# Make date sequence and store data in a data.frame for ggplot.
dates <- seq(as.Date("1955/12/1"), as.Date("2003/1/1"), by = "quarter")
col_names <- names(endog_data)
# Colors to use
col_regime_1 <- "#21618C"
col_regime_2 <- "#D68910"
irf_s1_plots <- list()
irf_s2_plots <- list()
fz_plots <- list()
# Loop to fill to create plots
plot_num <- 1
for (kk in 1:3){
# Convert matrices to tibble for ggplot
tbl_s1 <- tibble(x = 1:dim(results_s1_mean)[2], mean = results_s1_mean[kk, ],
low = results_s1_low[kk, ], up = results_s1_up[kk, ])
tbl_s2 <- tibble(x = 1:dim(results_s2_mean)[2], mean = results_s2_mean[kk, ],
low = results_s2_low[kk, ], up = results_s2_up[kk, ])
tbl_fz <- tibble(x = dates, fz = fz_mat[kk, ])
irf_s1_plots[[plot_num]] <- ggplot() +
geom_line(data = tbl_s1, aes(y = mean, x = x), col = col_regime_1) +
geom_ribbon(data = tbl_s1, aes(x = x, ymin = low, ymax = up), col = 'grey',
fill = 'grey', alpha = 0.3) +
theme_classic() +
ggtitle(paste("Regime 1: ", col_names[3], 'on', col_names[1], sep=" ")) +
xlab('') +
ylab('') +
theme(title = element_text(size = 8),
plot.title = element_text(hjust = 0.5)) +
# scale_y_continuous(expand = c(0, 0)) +
ylim(-1.2, 1.2) +
scale_x_continuous(expand = c(0, 0),
breaks = seq(0, hor, 2)) +
geom_hline(yintercept = 0, col = "black", size = 0.25, linetype = "dashed")
irf_s2_plots[[plot_num]] <- ggplot() +
geom_line(data = tbl_s2, aes(y = mean, x = x), col = col_regime_2) +
geom_ribbon(data = tbl_s2, aes(x = x, ymin = low, ymax = up), col = 'grey',
fill = 'grey', alpha = 0.3) +
theme_classic() +
ggtitle(paste("Regime 2: ", col_names[3], 'on', col_names[1], sep=" ")) +
xlab('') +
ylab('') +
theme(title = element_text(size = 8),
plot.title = element_text(hjust = 0.5)) +
# scale_y_continuous(expand = c(0, 0)) +
ylim(-1.3, 1.3) +
scale_x_continuous(expand = c(0, 0),
breaks = seq(0, hor, 2)) +
geom_hline(yintercept = 0, col = "black", size = 0.25, linetype = "dashed")
# Plot transition function
fz_plots[[plot_num]] <- ggplot(data = tbl_fz) +
geom_rect(data = nber_rec_se, aes(xmin = start, xmax = end,
ymin = 0, ymax = Inf, fill = "a"), alpha = 0.9) +
geom_line(aes(x = x, y = fz), size = .5) +
ggtitle("NBER dates and transition variable") +
theme_classic() +
theme(title = element_text(size = 8),
plot.title = element_text(hjust = 0.5),
legend.position = c(.5, -.5)) +
ylab("") +
xlab("") +
scale_x_date(date_breaks = "10 year", date_labels = "%Y",
expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_manual(name = "",
values = c("grey"),
labels = c("NBER Recessions"))
plot_num <- plot_num + 1
}
# Make column plots
a_1 <- ggarrange(fz_plots[[1]], irf_s1_plots[[1]], irf_s2_plots[[1]], ncol = 1, nrow = 3)
a_1 <- annotate_figure(a_1, bottom = text_grob(bquote(paste("a) Results for ", ~gamma == .(gamma_vals[1]))), face = "bold", size = 10, hjust = .3, vjust = .5))
a_2 <- ggarrange(fz_plots[[2]], irf_s1_plots[[2]], irf_s2_plots[[2]], ncol = 1, nrow = 3)
a_2 <- annotate_figure(a_2, bottom = text_grob(bquote(paste("b) Results for ", ~gamma == .(gamma_vals[2]))), face = "bold", size = 10, hjust = .3, vjust = .5))
a_3 <- ggarrange(fz_plots[[3]], irf_s1_plots[[3]], irf_s2_plots[[3]], ncol = 1, nrow = 3)
a_3 <- annotate_figure(a_3, bottom = text_grob(bquote(paste("c) Results for ", ~gamma == .(gamma_vals[3]))), face = "bold", size = 10, hjust = .3, vjust = .5))
# Combine columns
combine_plot <- ggarrange(a_1, a_2, a_3, ncol = 3)
combine_plot
#####################################################################################################
# --- Code for Figure 8 ---
#####################################################################################################
# Recession dates
start_rec <- c("1957 Q3", "1960 Q2", "1970 Q1", "1973 Q4", "1980 Q1", "1981 Q3", "1990 Q3", "2001 Q2")
end_rec <- c("1958 Q2", "1961 Q1", "1970 Q4", "1975 Q1", "1980 Q3", "1982 Q4", "1991 Q1", "2001 Q4")
# Quarterly fequence for Jordá data
dates <- as.yearqtr(seq(as.Date("1955/12/1"), as.Date("2003/1/1"), by = "quarter"))
nber_rec_se <- tibble(start = as.yearqtr(start_rec), end = as.yearqtr(end_rec)) %>%
filter(start %in% dates) %>%
mutate(start = as.Date(start)) %>%
mutate(end = as.Date(end))
# Convert back with as.Date for ggplot
dates <- as.Date(dates)
# Convert back with as.Date for ggplot
dates <- as.Date(dates)
# Load data from lpirfs package
endog_data <- interest_rules_var_data
switching_variable <- interest_rules_var_data$GDP_gap
hor <- 12
shock_pos <- 3
# Results for lpirfs
results_s1_mean <- matrix(NA, 3, hor + 1)
results_s1_low <- results_s1_mean
results_s1_up <- results_s1_mean
results_s2_mean <- matrix(NA, 3, hor + 1)
results_s2_low <- results_s2_mean
results_s2_up <- results_s2_mean
fz_mat <- matrix(NA, 3, dim(endog_data)[1] - 4)
# Choose values for lambda
lambda_vals <- c(6.25, 1600, 129600)
for(ii in seq_along(lambda_vals)){
# Estimate linear model with lpirfs function
results_nl <- lp_nl(endog_data,
lags_endog_lin = 4,
lags_endog_nl = 4,
trend = 0,
shock_type = 0,
switching = switching_variable,
use_hp = TRUE,
lambda = lambda_vals[ii],
gamma = 5,
confint = 1.96,
hor = 12,
num_cores = 1)
results_s1_mean[ii, ] <- results_nl$irf_s1_mean[1, , 3]
results_s1_low[ii, ] <- results_nl$irf_s1_low[1, , 3]
results_s1_up[ii, ] <- results_nl$irf_s1_up[1, , 3]
results_s2_mean[ii, ] <- results_nl$irf_s2_mean[1, , 3]
results_s2_low[ii, ] <- results_nl$irf_s2_low[1, , 3]
results_s2_up[ii, ] <- results_nl$irf_s2_up[1, , 3]
# fz_mat[ii, ] <- results_nl$fz
fz_mat[ii, ] <- hp_filter(matrix(switching_variable[(4+1):193]), lambda_vals[ii])[[1]]
}
col_names <- names(endog_data)
# Colors to use
col_regime_1 <- "#21618C"
col_regime_2 <- "#D68910"
irf_s1_plots <- list()
irf_s2_plots <- list()
fz_plots <- list()
# Loop to fill to create plots
plot_num <- 1
for (kk in 1:3){
# Convert matrices to tibble for ggplot
tbl_s1 <- tibble(x = 1:dim(results_s1_mean)[2], mean = results_s1_mean[kk, ],
low = results_s1_low[kk, ], up = results_s1_up[kk, ])
tbl_s2 <- tibble(x = 1:dim(results_s2_mean)[2], mean = results_s2_mean[kk, ],
low = results_s2_low[kk, ], up = results_s2_up[kk, ])
tbl_fz <- tibble(x = dates, fz = fz_mat[kk, ])
irf_s1_plots[[plot_num]] <- ggplot() +
geom_line(data = tbl_s1, aes(y = mean, x = x), col = col_regime_1) +
geom_ribbon(data = tbl_s1, aes(x = x, ymin = low, ymax = up), col = 'grey',
fill = 'grey', alpha = 0.3) +
theme_classic() +
ggtitle(paste("Regime 2: ", col_names[3], 'on', col_names[1], sep=" ")) +
xlab('') +
ylab('') +
theme(title = element_text(size = 8),
plot.title = element_text(hjust = 0.5)) +
# scale_y_continuous(expand = c(0, 0)) +
ylim(-1.7, 0.7) +
scale_x_continuous(expand = c(0, 0),
breaks = seq(0, hor, 2)) +
geom_hline(yintercept = 0, col = "black", size = 0.25, linetype = "dashed")
irf_s2_plots[[plot_num]] <- ggplot() +
geom_line(data = tbl_s2, aes(y = mean, x = x), col = col_regime_2) +
geom_ribbon(data = tbl_s2, aes(x = x, ymin = low, ymax = up), col = 'grey',
fill = 'grey', alpha = 0.3) +
theme_classic() +
ggtitle(paste("Regime 2: ", col_names[3], 'on', col_names[1], sep=" ")) +
xlab('') +
ylab('') +
theme(title = element_text(size = 8),
plot.title = element_text(hjust = 0.5)) +
# scale_y_continuous(expand = c(0, 0)) +
ylim(- 1.2, 0.8) +
scale_x_continuous(expand = c(0, 0),
breaks = seq(0, hor, 2)) +
geom_hline(yintercept = 0, col = "black", size = 0.25, linetype = "dashed")
# Plot transition function
fz_plots[[plot_num]] <- ggplot(data = tbl_fz) +
geom_rect(data = nber_rec_se, aes(xmin = start, xmax = end,
ymin = -Inf, ymax = Inf, fill = "a"), alpha = 0.9) +
geom_line(aes(x = x, y = fz), size = .5) +
ggtitle("NBER dates and cyclical HP component") +
theme_classic() +
theme(title = element_text(size = 8),
plot.title = element_text(hjust = 0.5),
legend.position = c(.5, -.5)) +
ylab("") +
xlab("") +
scale_x_date(date_breaks = "10 year", date_labels = "%Y",
expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_manual(name = "",
values = c("grey"),
labels = c("NBER Recessions"))
plot_num <- plot_num + 1
}
# Make column plots
a_1 <- ggarrange(fz_plots[[1]], irf_s1_plots[[1]], irf_s2_plots[[1]], ncol = 1, nrow = 3)
a_1 <- annotate_figure(a_1, bottom = text_grob(bquote(paste("a) Results for ", ~lambda == .(lambda_vals[1]))), face = "bold", size = 10, hjust = .3, vjust = .5))
a_2 <- ggarrange(fz_plots[[2]], irf_s1_plots[[2]], irf_s2_plots[[2]], ncol = 1, nrow = 3)
a_2 <- annotate_figure(a_2, bottom = text_grob(bquote(paste("b) Results for ", ~lambda == .(lambda_vals[2]))), face = "bold", size = 10, hjust = .3, vjust = .5))
a_3 <- ggarrange(fz_plots[[3]], irf_s1_plots[[3]], irf_s2_plots[[3]], ncol = 1, nrow = 3)
a_3 <- annotate_figure(a_3, bottom = text_grob(bquote(paste("c) Results for ", ~lambda == "129 600")), face = "bold", size = 10, hjust = .4, vjust = .5))
# Combine columns
combine_plot <- ggarrange(a_1, a_2, a_3, ncol = 3)
combine_plot
#####################################################################################################
# Comparing normal and Newey West standard errors
#####################################################################################################
# Load data from lpirfs package
endog_data <- interest_rules_var_data
hor <- 12
shock_pos <- 3
use_nw <- c(FALSE, TRUE, TRUE)
nw_prewhite <- c(FALSE, FALSE, TRUE)
# Results for lpirfs
results_irf_lpirfs_mean <- array(NA, c(dim(endog_data)[2], hor + 1, 3))
results_irf_lpirfs_low <- results_irf_lpirfs_mean
results_irf_lpirfs_up <- results_irf_lpirfs_mean
# Estimate irfs for Jordá method
for(ii in 1:3){
results_lin <- lp_lin(endog_data,
lags_endog_lin = 4,
trend = 0,
shock_type = 0,
confint = 1.96,
use_nw = use_nw[ii],
nw_prewhite = nw_prewhite[ii],
hor = 12)
results_irf_lpirfs_mean[, , ii] <- results_lin$irf_lin_mean[, , shock_pos]
results_irf_lpirfs_low[, , ii] <- results_lin$irf_lin_low[, , shock_pos]
results_irf_lpirfs_up[, , ii] <- results_lin$irf_lin_up[, , shock_pos]
}
shock_names <- colnames(endog_data)
gg_lin <- list()
x_labs <- c("a.) Normal Std. Errors", "b.) Newy West (1987)", "c.) Pre-whitened NW (1987)")
# Loop to fill to create plots
plot_num <- 1
for (kk in 1:3){
for (rr in 1:3){
# Extract relevant impulse responses
tbl_lpirfs_mean <- as.matrix(t(results_irf_lpirfs_mean[, 1:hor , kk]))[, rr]
tbl_lpirfs_low <- as.matrix(t(results_irf_lpirfs_low[, 1:hor , kk]))[, rr]
tbl_lpirfs_up <- as.matrix(t(results_irf_lpirfs_up[, 1:hor , kk]))[, rr]
# Convert to tibble for ggplot
tbl_lin_lpirfs <- tibble(x = seq_along(tbl_lpirfs_mean), mean = tbl_lpirfs_mean,
low = tbl_lpirfs_low, up = tbl_lpirfs_up)
gg_lin[[plot_num]] <- ggplot()+
geom_line(data = tbl_lin_lpirfs, aes(y = mean, x = x)) + # , linetype = "a", color = "a"
geom_ribbon(data = tbl_lin_lpirfs, aes(x = x, ymin = low, ymax = up), col = 'grey',
fill = 'grey', alpha = 0.3) +
theme_classic() +
ggtitle(paste(shock_names[shock_pos], 'on', shock_names[rr], sep=" ")) +
xlab('') +
ylab('') +
theme(title = element_text(size = 6),
plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 8, face="bold")) +
scale_x_continuous(expand = c(0, 0),
breaks = seq(0, hor, 2)) +
geom_hline(yintercept = 0, col = "black", size = 0.25, linetype = "dashed")
if(plot_num %in% c(1, 4, 7)) gg_lin[[plot_num]] <- gg_lin[[plot_num]] + ylim(-1, .2)
if(plot_num %in% c(2, 5, 8)) gg_lin[[plot_num]] <- gg_lin[[plot_num]] + ylim(- .85, .5)
if(plot_num %in% c(3, 6, 9)) gg_lin[[plot_num]] <- gg_lin[[plot_num]] + ylim(- .7, 1.2)
if(!(plot_num %in% c(3, 6, 9))) gg_lin[[plot_num]] <- gg_lin[[plot_num]] +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
# Add one to count variable
plot_num <- plot_num + 1
}
}
# Make column plots
a_1 <- ggarrange(gg_lin[[1]], gg_lin[[2]], gg_lin[[3]], ncol = 1, nrow = 3, common.legend = TRUE)
a_1 <- annotate_figure(a_1, bottom = text_grob(x_labs[1], size = 8, hjust = .3, vjust = -1))
a_2 <- ggarrange(gg_lin[[4]], gg_lin[[5]], gg_lin[[6]], ncol = 1, nrow = 3, common.legend = TRUE)
a_2 <- annotate_figure(a_2, bottom = text_grob(x_labs[2], size = 8, hjust = .3, vjust = -1))
a_3 <- ggarrange(gg_lin[[7]], gg_lin[[8]], gg_lin[[9]], ncol = 1, nrow = 3, common.legend = TRUE)
a_3 <- annotate_figure(a_3, bottom = text_grob(x_labs[3], size = 8, hjust = .3, vjust = -1))
# Combine columns
combine_plot <- ggarrange(a_1, a_2, a_3, ncol = 3)
combine_plot
