Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for a GECM(1,1) model, assuming the underlying model is in first differences (x.vrbl.d.x = y.vrbl.d.y = 0 and x.d.vrbl.d.x = y.d.vrbl.d.y = 1) and the user wants a marginal effect (the untransformed GDRF) and inferences about y in levels to a treatment applied to x in levels. (This is just a wrapper for GDRF.gecm.plot with simplifying assumptions)

Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for a GECM(1,1) model, assuming the underlying model is in first differences (x.vrbl.d.x = y.vrbl.d.y = 0 and x.d.vrbl.d.x = y.d.vrbl.d.y = 1) and the user wants a marginal effect (the untransformed GDRF) and inferences about y in levels to a treatment applied to x in levels. (This is just a wrapper for GDRF.gecm.plot with simplifying assumptions)

Usage

gecm.plot(
  model = NULL,
  x.vrbl = NULL,
  y.vrbl = NULL,
  x.d.vrbl = NULL,
  y.d.vrbl = NULL,
  shock.history = "pulse",
  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 numeric 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 numeric 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.d.vrbl: a named numeric 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 numeric vector of the y variables (of the higher level of differencing, usually first differences d = 1) and corresponding lag orders in the GECM model. Can be NULL if the model has no lags of the differenced dependent variables
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
dM.level: a numeric significance level 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: a string for the type of standard error to extract from the model. The default is const, but any argument to vcovHC from the sandwich package is accepted
return.data: logical to return the raw calculated GDRFs as a list element under estimates. The default is FALSE
return.plot: logical to return the visualized GDRFs as a list element under plot. The default is TRUE
return.formulae: logical to 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

Value

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

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

Author

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

Examples

# GECM(1,1). So we can use gecm.plot to quickly check dynamics
# 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 <- gecm.plot(model = model,
                                  x.vrbl = c("l_1_x" = 1), 
                                  y.vrbl = c("l_1_y" = 1),
                                  x.d.vrbl = c("d_x" = 0, "l_1_d_x" = 1),
                                  y.d.vrbl = c("l_1_d_y" = 1),
                                  shock.history = "pulse", 
                                  s.limit = 10, 
                                  return.plot = TRUE, 
                                  return.formulae = TRUE)
names(test.pulse)
#> [1] "plot"      "formulae"  "binomials"