# Replications and applications with lpirfs

#### Philipp Adämmer

#### 2025-10-01

## Purpose

This vignette shows how to use lpirfs by replicating the figures of the paper
“lpirfs: An R-Package to Estimate Impulse Response Functions by Local Projections”

## 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