GDRF.gecm.plot: Evaluate (and possibly plot) the General Dynamic Treatment...

In tseffects: Dynamic Inferences from Time Series (with Interactions)

Evaluate (and possibly plot) the General Dynamic Treatment Effect (GDRF) for a Generalized Error Correction Model (GECM)

Description

Evaluate (and possibly plot) the General Dynamic Treatment Effect (GDRF) for a Generalized Error Correction Model (GECM)

Usage

GDRF.gecm.plot(
  model = NULL,
  x.vrbl = NULL,
  y.vrbl = NULL,
  x.vrbl.d.x = NULL,
  y.vrbl.d.y = NULL,
  x.d.vrbl = NULL,
  y.d.vrbl = NULL,
  x.d.vrbl.d.x = NULL,
  y.d.vrbl.d.y = NULL,
  shock.history = "pulse",
  inferences.y = "levels",
  inferences.x = "levels",
  dM.level = 0.95,
  s.limit = 20,
  se.type = "const",
  return.data = FALSE,
  return.plot = TRUE,
  return.formulae = FALSE,
  ...
)

Arguments

model	the lm model containing the GECM estimates
x.vrbl	a named vector of the x variables (of the lower level of differencing, usually in levels d = 0) and corresponding lag orders in the GECM model
y.vrbl	a named vector of the (lagged) y variables (of the lower level of differencing, usually in levels d = 0) and corresponding lag orders in the GECM model
x.vrbl.d.x	the order of differencing of the x variable (of the lower level of differencing, usually in levels d = 0) in the GECM model
y.vrbl.d.y	the order of differencing of the y variable (of the lower level of differencing, usually in levels d = 0) in the GECM model
x.d.vrbl	a named vector of the x variables (of the higher level of differencing, usually first differences d = 1) and corresponding lag orders in the GECM model
y.d.vrbl	a named vector of the y variables (of the higher level of differencing, usually first differences d = 1) and corresponding lag orders in the GECM model
x.d.vrbl.d.x	the order of differencing of the x variable (of the higher level of differencing, usually first differences d = 1) in the GECM model
y.d.vrbl.d.y	the order of differencing of the y variable (of the higher level of differencing, usually first differences d = 1) in the GECM model
shock.history	the desired shock history. shock.history determines the shock history (h) that will be applied to the independent variable. -1 represents a pulse. 0 represents a step. These can also be specified via pulse and step. For others, see Vande Kamp, Jordan, and Rajan. The default is pulse
inferences.y	does the user want resulting inferences about the dependent variable in levels or in differences? The default is levels
inferences.x	does the user want to apply the shock history to the independent variable in levels or in differences? The default is levels
dM.level	level of significance of the GDRF, calculated by the delta method. The default is 0.95
s.limit	an integer for the number of periods to determine the GDRF (beginning at s = 0)
se.type	the type of standard error to extract from the GECM model. The default is const, but any argument to vcovHC from the sandwich package is accepted
return.data	return the raw calculated GDRFs as a list element under estimates. The default is FALSE
return.plot	return the visualized GDRFs as a list element under plot. The default is TRUE
return.formulae	return the formulae for the GDRFs as a list element under formulae (for the GDRFs) and binomials (for the shock history). The default is FALSE
...	other arguments to be passed to the call to plot

Details

We assume that the GECM model estimated is well specified, free of residual autocorrelation, balanced, and meets other standard time-series qualities. Given that, to obtain inferences for the specified shock history, the user only needs a named vector of the x and y variables, as well as the order of the differencing. Internally, the GECM to ADL equivalences are used to calculate the GDRFs from the GECM

Value

depending on return.data, return.plot, and return.formulae, a list of elements relating to the GDTE

Author(s)

Soren Jordan, Garrett N. Vande Kamp, and Reshi Rajan

Examples

# GECM(1,1)
# Use the toy data to run a GECM. No argument is made this 
#  is well specified or even sensible; it is just expository
model <- lm(d_y ~ l_1_y + l_1_x + l_1_d_y + d_x + l_1_d_x, data = toy.ts.interaction.data)
test.pulse <- GDRF.gecm.plot(model = model,
                                  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 = 10, 
                                  return.plot = TRUE, 
                                  return.formulae = TRUE)
names(test.pulse)

tseffects documentation built on Feb. 5, 2026, 5:09 p.m.