## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----------------------------------------------------------------------------- library(tseffects) ADL.finite <- pulse.calculator(x.vrbl = c("mood" = 0, "l1_mood" = 1, "l2_mood" = 2), y.vrbl = NULL, limit = 5) ## ----------------------------------------------------------------------------- ADL1.2.pulses <- pulse.calculator(x.vrbl = c("mood" = 0, "l1_mood" = 1, "l2_mood" = 2), y.vrbl = c("l1_policy" = 1), limit = 5) ## ----------------------------------------------------------------------------- general.calculator(d.x = 0, d.y = 0, h = -1, limit = 5, pulses = ADL1.2.pulses) ## ----------------------------------------------------------------------------- ADL1.2.d.pulses <- pulse.calculator(x.vrbl = c("d_mood" = 0, "l1_d_mood" = 1, "l2_d_mood" = 2), y.vrbl = c("l1_policy" = 1), limit = 5) ## ----------------------------------------------------------------------------- general.calculator(d.x = 1, d.y = 0, h = -1, limit = 5, pulses = ADL1.2.d.pulses) ## ----------------------------------------------------------------------------- general.calculator(d.x = 1, d.y = 0, h = 1, limit = 5, pulses = ADL1.2.d.pulses) ## ----------------------------------------------------------------------------- data(toy.ts.interaction.data) # Fit an ADL(1, 1) model.adl <- lm(y ~ l_1_y + x + l_1_x, data = toy.ts.interaction.data) test.pulse <- GDRF.adl.plot(model = model.adl, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_y" = 1), d.x = 0, d.y = 0, shock.history = "pulse", inferences.y = "levels", inferences.x = "levels", s.limit = 20, return.plot = TRUE, return.formulae = TRUE) ## ----------------------------------------------------------------------------- test.pulse2 <- GDRF.adl.plot(model = model.adl, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_y" = 1), d.x = 0, d.y = 0, shock.history = "step", inferences.y = "levels", inferences.x = "levels", s.limit = 20, return.plot = TRUE, return.formulae = TRUE) ## ----------------------------------------------------------------------------- data(toy.ts.interaction.data) # Fit an ADL(1, 1) model.adl.diffs <- lm(y ~ l_1_y + d_x + l_1_d_x, data = toy.ts.interaction.data) ## ----------------------------------------------------------------------------- GDRF.adl.plot(model = model.adl.diffs, x.vrbl = c("d_x" = 0, "l_1_d_x" = 1), y.vrbl = c("l_1_y" = 1), d.x = 1, d.y = 0, shock.history = "pulse", inferences.y = "levels", inferences.x = "levels", s.limit = 20, return.plot = TRUE) ## ----------------------------------------------------------------------------- GDRF.adl.plot(model = model.adl.diffs, x.vrbl = c("d_x" = 0, "l_1_d_x" = 1), y.vrbl = c("l_1_y" = 1), d.x = 1, d.y = 0, shock.history = "pulse", inferences.y = "levels", inferences.x = "differences", s.limit = 20, return.plot = TRUE) ## ----------------------------------------------------------------------------- # Fit an ADL(1, 1) model.adl <- lm(y ~ l_1_y + x + l_1_x, data = toy.ts.interaction.data) test.pulse <- GDRF.adl.plot(model = model.adl, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_y" = 1), d.x = 0, d.y = 0, shock.history = "pulse", inferences.y = "levels", inferences.x = "levels", effect.type = "fitted", prediction.values = list("x" = mean(toy.ts.interaction.data$x, na.rm = TRUE), "l_1_x" = mean(toy.ts.interaction.data$x, na.rm = TRUE)), shock.size = 1, s.limit = 20, return.plot = TRUE, return.formulae = TRUE) ## ----------------------------------------------------------------------------- # Fit a model in differences model.differences <- lm(d_y ~ l_1_d_y + x + l_1_x, data = toy.ts.interaction.data) GDRF.adl.plot(model = model.differences, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_d_y" = 1), d.x = 0, d.y = 1, shock.history = "pulse", inferences.y = "levels", inferences.x = "levels", effect.type = "fitted", baseline.y = 3, shock.size = 1, s.limit = 20, return.plot = TRUE, return.formulae = FALSE) ## ----------------------------------------------------------------------------- GDRF.adl.plot(model = model.differences, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_d_y" = 1), d.x = 0, d.y = 1, shock.history = "pulse", inferences.y = "levels", inferences.x = "levels", effect.type = "fitted", baseline.y = 3, baseline.y.se = sd(toy.ts.interaction.data$y, na.rm = TRUE), shock.size = 1, s.limit = 20, return.plot = TRUE, return.formulae = FALSE) ## ----------------------------------------------------------------------------- # Fit a GECM(1, 1) model.gecm <- lm(d_y ~ l_1_y + l_1_d_y + l_1_x + d_x + l_1_d_x, data = toy.ts.interaction.data) ## ----------------------------------------------------------------------------- gecm.pulse <- GDRF.gecm.plot(model = model.gecm, x.vrbl = c("l_1_x" = 1), y.vrbl = c("l_1_y" = 1), x.vrbl.d.x = 0, y.vrbl.d.y = 0, x.d.vrbl = c("d_x" = 0, "l_1_d_x" = 1), y.d.vrbl = c("l_1_d_y" = 1), x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, shock.history = "pulse", inferences.y = "levels", inferences.x = "levels", s.limit = 20, return.plot = TRUE, return.formulae = TRUE) ## ----------------------------------------------------------------------------- data(toy.ts.interaction.data) # Fit an ADL(1, 1) interact.model <- lm(y ~ l_1_y + x + l_1_x + z + l_1_z + x_z + z_l_1_x + x_l_1_z + l_1_x_l_1_z, data = toy.ts.interaction.data) ## ----------------------------------------------------------------------------- interact.adl.plot(model = interact.model, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_y" = 1), z.vrbl = c("z" = 0, "l_1_z" = 1), x.z.vrbl = c("x_z" = 0, "z_l_1_x" = 1, "x_l_1_z" = 0, "l_1_x_l_1_z" = 1), z.vals = -1:1, effect.type = "impulse", plot.type = "lines", line.options = "z.lines", s.limit = 20) ## ----------------------------------------------------------------------------- interact.adl.plot(model = interact.model, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_y" = 1), z.vrbl = c("z" = 0, "l_1_z" = 1), x.z.vrbl = c("x_z" = 0, "z_l_1_x" = 1, "x_l_1_z" = 0, "l_1_x_l_1_z" = 1), z.vals = c(-1, 1), s.vals = c(0, 5), effect.type = "impulse", plot.type = "lines", line.options = "s.lines", s.limit = 20) ## ----------------------------------------------------------------------------- interact.adl.plot(model = interact.model, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_y" = 1), z.vrbl = c("z" = 0, "l_1_z" = 1), x.z.vrbl = c("x_z" = 0, "z_l_1_x" = 1, "x_l_1_z" = 0, "l_1_x_l_1_z" = 1), z.vals = c(-1, 1), s.vals = c(0, 5), effect.type = "step", plot.type = "lines", line.options = "s.lines", s.limit = 20) ## ----------------------------------------------------------------------------- interact.adl.plot(model = interact.model, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_y" = 1), z.vrbl = c("z" = 0, "l_1_z" = 1), x.z.vrbl = c("x_z" = 0, "z_l_1_x" = 1, "x_l_1_z" = 0, "l_1_x_l_1_z" = 1), effect.type = "pulse", plot.type = "heatmap", heatmap.options = "all", s.limit = 20) ## ----------------------------------------------------------------------------- interact.adl.plot(model = interact.model, x.vrbl = c("x" = 0, "l_1_x" = 1), y.vrbl = c("l_1_y" = 1), z.vrbl = c("z" = 0, "l_1_z" = 1), x.z.vrbl = c("x_z" = 0, "z_l_1_x" = 1, "x_l_1_z" = 0, "l_1_x_l_1_z" = 1), effect.type = "pulse", plot.type = "heatmap", heatmap.options = "significant", s.limit = 20) ## ----------------------------------------------------------------------------- library(ARDL) ardl_3132 <- ardl(LRM ~ LRY + IBO + IDE, data = denmark, order = c(3,1,3,2)) uecm_3132 <- uecm(ardl_3132) mult_uecm <- multipliers(uecm_3132) mult_uecm mult_inter12 <- multipliers(uecm_3132, type = 12, se = TRUE) mult_inter12 mult_uecm_sr <- multipliers(uecm_3132, type = "sr") mult_uecm_sr ## ----------------------------------------------------------------------------- library(tidyverse) library(zoo) denmark_replication <- fortify.zoo(denmark) denmark_replication$LRM_l1 <- lag(denmark_replication$LRM) denmark_replication$LRM_d1 <- denmark_replication$LRM - denmark_replication$LRM_l1 denmark_replication$LRM_d1_l1 <- lag(denmark_replication$LRM_d1) denmark_replication$LRM_d1_l2 <- lag(denmark_replication$LRM_d1, 2) denmark_replication$IBO_l1 <- lag(denmark_replication$IBO) denmark_replication$IBO_d1 <- denmark_replication$IBO - denmark_replication$IBO_l1 denmark_replication$IBO_d1_l1 <- lag(denmark_replication$IBO_d1) denmark_replication$IBO_d1_l2 <- lag(denmark_replication$IBO_d1, 2) denmark_replication$LRY_l1 <- lag(denmark_replication$LRY) denmark_replication$LRY_d1 <- denmark_replication$LRY - denmark_replication$LRY_l1 denmark_replication$IDE_l1 <- lag(denmark_replication$IDE) denmark_replication$IDE_d1 <- denmark_replication$IDE - denmark_replication$IDE_l1 denmark_replication$IDE_d1_l1 <- lag(denmark_replication$IDE_d1) uecm_3132_replication <- lm(LRM_d1 ~ LRM_l1 + LRM_d1_l1 + LRM_d1_l2 + LRY_l1 + LRY_d1 + IDE_l1 + IDE_d1 + IDE_d1_l1 + IBO_l1 + IBO_d1 + IBO_d1_l1 + IBO_d1_l2, data = denmark_replication) ## ----------------------------------------------------------------------------- mult_inter12_IBO2 <- GDRF.gecm.plot(model = uecm_3132_replication, x.vrbl = c("IBO_l1" = 1), y.vrbl = c("LRM_l1" = 1), x.d.vrbl = c("IBO_d1" = 0, "IBO_d1_l1" = 1, "IBO_d1_l2" = 2), x.vrbl.d.x = 0, y.vrbl.d.y = 0, y.d.vrbl = c("LRM_d1_l1" = 1, "LRM_d1_l2" = 2), shock.history = "step", x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, s.limit = 12, return.data = TRUE, return.plot = FALSE) all.equal(mult_inter12_IBO2$GDRF[14], mult_uecm$Estimate[3]) all.equal(mult_inter12_IBO2$SE[14], mult_uecm$'Std. Error'[3]) all.equal(mult_inter12_IBO2$GDRF[1:13], mult_inter12$IBO$Interim) ## ----------------------------------------------------------------------------- mult_inter12_IBO <- GDRF.gecm.plot(model = uecm_3132_replication, x.vrbl = c("IBO_l1" = 1), y.vrbl = c("LRM_l1" = 1), x.d.vrbl = c("IBO_d1" = 0, "IBO_d1_l1" = 1, "IBO_d1_l2" = 2), x.vrbl.d.x = 0, y.vrbl.d.y = 0, y.d.vrbl = c("LRM_d1_l1" = 1, "LRM_d1_l2" = 2), shock.history = "pulse", x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, s.limit = 12, return.data = TRUE, return.plot = FALSE) all.equal(mult_inter12_IBO$GDRF, mult_inter12$IBO$Delay) all.equal(mult_inter12_IBO$SE, mult_inter12$IBO$'Std. Error Delay') ## ----------------------------------------------------------------------------- all.equal(mult_inter12_IBO$SE[1], sqrt(vcov(uecm_3132_replication)[11,11])) ## ----------------------------------------------------------------------------- all.equal(mult_inter12$IBO$'Std. Error Delay'[1], sqrt(vcov(uecm_3132)[9,9])) all.equal(mult_inter12$IBO$'Std. Error Delay'[1], mult_uecm_sr$'Std. Error'[3]) all.equal(sqrt(vcov(uecm_3132)[9,9]), mult_uecm_sr$'Std. Error'[3]) ## ----------------------------------------------------------------------------- all.equal(mult_inter12_IBO$SE[1], mult_uecm_sr$'Std. Error'[3]) ## ----eval = FALSE------------------------------------------------------------- # ### Replication - LRY # # mult_inter12_LRY2 <- GDRF.gecm.plot(model = uecm_3132_replication, x.vrbl = c("LRY_l1" = 1), # y.vrbl = c("LRM_l1" = 1), # x.d.vrbl = c("LRY_d1" = 0), # x.vrbl.d.x = 0, y.vrbl.d.y = 0, # y.d.vrbl = c("LRM_d1_l1" = 1, "LRM_d1_l2" = 2), # shock.history = "step", # x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, # s.limit = 12, return.data = TRUE, return.plot = FALSE) # # all.equal(mult_inter12_LRY2$GDRF[1:13], mult_inter12$LRY$Interim) # Estimates are Identical # # ARDL does not calculate standard errors for SRF # all.equal(mult_inter12_LRY2$GDRF[14], mult_uecm$Estimate[2]) # Estimates are Identical # all.equal(mult_inter12_LRY2$SE[14], mult_uecm$'Std. Error'[2]) # Standard Errors are Identical # # # mult_inter12_LRY <- GDRF.gecm.plot(model = uecm_3132_replication, x.vrbl = c("LRY_l1" = 1), # y.vrbl = c("LRM_l1" = 1), # x.d.vrbl = c("LRY_d1" = 0), # x.vrbl.d.x = 0, y.vrbl.d.y = 0, # y.d.vrbl = c("LRM_d1_l1" = 1, "LRM_d1_l2" = 2), # shock.history = "pulse", # x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, # s.limit = 12, return.data = TRUE, return.plot = FALSE) # # all.equal(mult_inter12_LRY$GDRF, mult_inter12$LRY$Delay) # Estimates are Identical # all.equal(mult_inter12_LRY$SE, mult_inter12$LRY$'Std. Error Delay') # Standard errors are not # # all.equal(mult_inter12_LRY$SE[1], sqrt(vcov(uecm_3132_replication)[6,6])) # IRF(0) SE match Coef SE for tseffects # # all.equal(mult_inter12$LRY$'Std. Error Delay'[1], sqrt(vcov(uecm_3132)[8,8])) # IRF(0) SE do not match Coef SE for ARDL # all.equal(mult_inter12$LRY$'Std. Error Delay'[1], mult_uecm_sr$'Std. Error'[2]) # IRF(0) SE do not match SRE SE for ARDL # all.equal(sqrt(vcov(uecm_3132)[8,8]), mult_uecm_sr$'Std. Error'[2]) # SRE SE are Coef SE for ARDL # # # # ### Replication - IDE # mult_inter12_IDE2 <- GDRF.gecm.plot(model = uecm_3132_replication, # x.vrbl = c("IDE_l1" = 1), y.vrbl = c("LRM_l1" = 1), # x.d.vrbl = c("IDE_d1" = 0, "IDE_d1_l1" = 1), # x.vrbl.d.x = 0, y.vrbl.d.y = 0, # y.d.vrbl = c("LRM_d1_l1" = 1, "LRM_d1_l2" = 2), # shock.history = "step", # x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, # s.limit = 12, return.data = TRUE, return.plot = FALSE) # # all.equal(mult_inter12_IDE2$GDRF[1:13], mult_inter12$IDE$Interim) # Estimates are Identical # # ARDL does not calculate standard errors for SRF # all.equal(mult_inter12_IDE2$GDRF[14], mult_uecm$Estimate[4]) # Estimates are Identical # all.equal(mult_inter12_IDE2$SE[14], mult_uecm$'Std. Error'[4]) # Standard Errors are Identical # # # mult_inter12_IDE <- GDRF.gecm.plot(model = uecm_3132_replication, # x.vrbl = c("IDE_l1" = 1), y.vrbl = c("LRM_l1" = 1), # x.d.vrbl = c("IDE_d1" = 0, "IDE_d1_l1" = 1), # x.vrbl.d.x = 0, y.vrbl.d.y = 0, # y.d.vrbl = c("LRM_d1_l1" = 1, "LRM_d1_l2" = 2), # shock.history = "pulse", # x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, # s.limit = 12, return.data = TRUE, return.plot = FALSE) # # all.equal(mult_inter12_IDE$GDRF, mult_inter12$IDE$Delay) # Estimates are Identical # all.equal(mult_inter12_IDE$SE, mult_inter12$IDE$'Std. Error Delay') # Standard Errors are not # # all.equal(mult_inter12_IDE$SE[1], sqrt(vcov(uecm_3132_replication)[8,8])) # IRF(0) SE match Coef SE # # all.equal(mult_inter12$IDE$'Std. Error Delay'[1], sqrt(vcov(uecm_3132)[12,12])) # IRF(0) SE do not match Coef SE # all.equal(mult_inter12$IDE$'Std. Error Delay'[1], mult_uecm_sr$'Std. Error'[4]) # IRF(0) SE do not match SRE SE # all.equal(sqrt(vcov(uecm_3132)[12,12]), mult_uecm_sr$'Std. Error'[4]) # SRE SE are Coef SE ## ----eval = FALSE------------------------------------------------------------- # library(kardl) # # kardl_model <- kardl(imf_example_data, # CPI ~ ER + PPI + asy(ER) + # det(covid) + trend, # mode = c(1, 2, 3, 0)) # # summary(kardl_model) # # long <- kardl_longrun(kardl_model) # long_summary <- summary(long) # # boot <- bootstrap(kardl_model, replications=5, seed = 123L) # # data <- imf_example_data # data$trend <- 1:nrow(data) # # data$L1.CPI <- lag(data$CPI) # data$L0.d.CPI <- data$CPI - data$L1.CPI # data$L1.d.CPI <- lag(data$L0.d.CPI) # # data$L1.ER <- lag(data$ER) # data$L0.d.ER <- data$ER - data$L1.ER # # data$L0.d.ER_POS <- ifelse(data$L0.d.ER>0, data$L0.d.ER, 0) # data$L1.d.ER_POS <- lag(data$L0.d.ER_POS) # data$L2.d.ER_POS <- lag(data$L0.d.ER_POS, 2) # # data$L0.d.ER_NEG <- ifelse(data$L0.d.ER>0, 0, data$L0.d.ER) # data$L1.d.ER_NEG <- lag(data$L0.d.ER_NEG) # data$L2.d.ER_NEG <- lag(data$L0.d.ER_NEG, 2) # data$L3.d.ER_NEG <- lag(data$L0.d.ER_NEG, 3) # # data$L1.ER_POS <- lag(cumsum(replace(data$L0.d.ER_POS, # is.na(data$L0.d.ER_POS), 0))) # data$L1.ER_NEG <- lag(cumsum(replace(data$L0.d.ER_NEG, # is.na(data$L0.d.ER_NEG), 0))) # # data$L1.PPI <- lag(data$PPI) # data$L0.d.PPI <- data$PPI - data$L1.PPI # # model <- lm(L0.d.CPI ~ L1.CPI + L1.ER_POS + L1.ER_NEG + L1.PPI + # L1.d.CPI + # L0.d.ER_POS + L1.d.ER_POS + L2.d.ER_POS + # L0.d.ER_NEG + L1.d.ER_NEG + L2.d.ER_NEG + L3.d.ER_NEG + # L0.d.PPI + # covid + trend, data = data[6:470,]) # # summary(model) # # boot_ppi <- GDRF.gecm.plot(model = model, # x.vrbl = c("L1.PPI" = 1), y.vrbl = c("L1.CPI" = 1), # x.d.vrbl = c("L0.d.PPI" = 0), # x.vrbl.d.x = 0, y.vrbl.d.y = 0, # y.d.vrbl = c("L1.d.CPI" = 1), shock.history = "step", # x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, # s.limit = 20, return.data = TRUE, return.plot = FALSE) # # all.equal(boot_ppi$GDRF[1:21], boot$mpsi$PPI_POS[1:21]) # Estimates are Identical # # all.equal(boot_ppi$GDRF[22], as.numeric(long$coefficients[3])) # Estimates are Identical # all.equal(boot_ppi$SE[22], long_summary$coefficients[3,2]) # Standard Errors are Identical ## ----eval = FALSE------------------------------------------------------------- # ### Replication - ER_POS # boot_pos <- GDRF.gecm.plot(model = model, # x.vrbl = c("L1.ER_POS" = 1), y.vrbl = c("L1.CPI" = 1), # x.d.vrbl = c("L0.d.ER_POS" = 0, "L1.d.ER_POS" = 1, "L2.d.ER_POS" = 2), # x.vrbl.d.x = 0, y.vrbl.d.y = 0, # y.d.vrbl = c("L1.d.CPI" = 1), shock.history = "step", # x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, # s.limit = 20, return.data = TRUE, return.plot = FALSE) # # all.equal(boot_pos$GDRF[1:21], boot$mpsi$ER_POS[1:21]) # Estimates are Identical # # all.equal(boot_pos$GDRF[22], as.numeric(long$coefficients[1])) # Estimates are Identical # all.equal(boot_pos$SE[22], long_summary$coefficients[1,2]) # Standard Errors are Identical # # # ### Replication - ER_NEG # boot_neg <- GDRF.gecm.plot(model = model, # x.vrbl = c("L1.ER_NEG" = 1), y.vrbl = c("L1.CPI" = 1), # x.d.vrbl = c("L0.d.ER_NEG" = 0, "L1.d.ER_NEG" = 1, # "L2.d.ER_NEG" = 2, "L3.d.ER_NEG" = 3), # x.vrbl.d.x = 0, y.vrbl.d.y = 0, # y.d.vrbl = c("L1.d.CPI" = 1), shock.history = "step", # x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, # s.limit = 20, return.data = TRUE, return.plot = FALSE) # # all.equal(-boot_neg$GDRF[1:21], boot$mpsi$ER_NEG[1:21]) # Estimates are Identical # # Must flip sign of tseffect estimates because it's for a one-unit decrease # # all.equal(boot_neg$GDRF[22], as.numeric(long$coefficients[2])) # Estimates are Identical # all.equal(boot_neg$SE[22], long_summary$coefficients[2,2]) # Standard Errors are Identical ## ----eval = FALSE------------------------------------------------------------- # library(dynamac) # # ### From helpfile of dynardl.simulation.plot() # # set.seed(1) # ardl.model <- dynardl(concern ~ incshare10 + urate, data = ineq, # lags = list("concern" = 1, "incshare10" = 1), # diffs = c("incshare10", "urate"), # lagdiffs = list("concern" = 1), # ec = TRUE, simulate = TRUE, range = 30, # shockvar = "incshare10", shockval = 1, fullsims = TRUE) # # summary(ardl.model) # # ### Replication - Model # inequality <- ineq # # inequality$l1.concern <- lag(inequality$concern, 1) # inequality$d1.concern <- inequality$concern-inequality$l1.concern # inequality$l1.d1.concern <- lag(inequality$d1.concern) # # inequality$l1.incshare10 <- lag(inequality$incshare10) # inequality$d1.incshare10 <- inequality$incshare10 - inequality$l1.incshare10 # # inequality$l1.urate <- lag(inequality$urate) # inequality$d1.urate <- inequality$urate - inequality$l1.urate # # ardl.model.replication <- lm(d1.concern ~ l1.concern + l1.d1.concern + # l1.incshare10 + d1.incshare10 + # d1.urate, data = inequality) # # ### Replication - incshare10 # ardl.model.replication.plot <- GDRF.gecm.plot(model = ardl.model.replication, # x.vrbl = c("l1.incshare10" = 1), y.vrbl = c("l1.concern" = 1), # x.d.vrbl = c("d1.incshare10" = 0), # x.vrbl.d.x = 0, y.vrbl.d.y = 0, # y.d.vrbl = c("l1.d1.concern" = 1), shock.history = "pulse", # x.d.vrbl.d.x = 1, y.d.vrbl.d.y = 1, # s.limit = 20, return.data = TRUE, return.plot = FALSE) ## ----eval = FALSE------------------------------------------------------------- # # dynamac is fitted values; dshift (difference) is akin to marginal effect (change in fitted) # all.equal(ardl.model.replication.plot$GDRF[1:21], # dshift(ardl.model$simulation$central)[10:30]) ## ----eval = FALSE------------------------------------------------------------- # dynamac.fitted <- dynardl(y ~ x, # lags = list("y" = 1, "x" = 1), # levels = c("x"), # ec = FALSE, simulate = TRUE, range = 35, # shockvar = "x", shockval = 1, # forceset = list("x" = 0.1), fullsims = TRUE, # data = toy.ts.interaction.data) # # model.fitted <- lm(y ~ l_1_y + x + l_1_x, data = toy.ts.interaction.data) # tseffects.fitted <- GDRF.adl.plot(model = model.fitted, # x.vrbl = c("x" = 0, "l_1_x" = 1), # y.vrbl = c("l_1_y" = 1), # d.y = 0, d.x = 0, # # baseline.y = 0, # effect.type = "fitted", shock.history = "step", # inferences.x = "levels", inferences.y = "levels", # prediction.values = list("x" = 0.1, "l_1_x" = 0.1), return.data = TRUE, return.plot = TRUE) # # # shock at 10, so baseline at 9. s = 0 is 10. thus s = 20 is 30. # cbind(tseffects.fitted$data$GDRF, dynamac.fitted$simulation$central[9:30]) # estimates are close # all.equal(tseffects.fitted$data$GDRF, dynamac.fitted$simulation$central[9:30]) # estimates are close # # library(ggplotify) # library(patchwork) # # combined <- as.ggplot(~dynardl.simulation.plot(dynamac.fitted)) + tseffects.fitted$plot # combined