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

Usage

ts.ci.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,
  te.type = "pte",
  inferences.y = "levels",
  inferences.x = "levels",
  dM.level = 0.95,
  h.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
te.type: the desired treatment history. te.type determines the counterfactual series that will be applied to the independent variable. -1 represents a Pulse Treatment Effect (PTE). 0 represents a Step Treatment Effect (STE). These can also be specified via pte, pulse, ste, and step. For others, see Vande Kamp, Jordan, and Rajan. The default is pte
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 counterfactual treatment to the independent variable in levels or in differences? The default is levels
dM.level: level of significance of the GDTE, calculated by the delta method. The default is 0.95
h.limit: limit an integer for the number of periods to determine the GDTE (beginning at 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 GDTEs as a list element under estimates. The default is FALSE
return.plot: return the visualized GDTEs as a list element under plot. The default is TRUE
return.formulae: return the formulae for the GDTEs as a list element under formulae (for the GDTEs) and binomials (for the treatment history). The default is FALSE
...: other arguments to be passed to the call to plot

Value

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

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 causal inferences for the specified treatment 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 GDTEs from the GECM

Author

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

Examples

# ADL(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 <- ts.ci.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,
                                  te.type = "pulse", 
                                  inferences.y = "levels", 
                                  inferences.x = "levels",
                                  h.limit = 10, 
                                  return.plot = TRUE, 
                                  return.formulae = TRUE)
names(test.pulse)
#> [1] "plot"      "formulae"  "binomials"