Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for an autoregressive distributed lag (ADL) model

Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for an autoregressive distributed lag (ADL) model

Usage

GDRF.adl.plot(
  model = NULL,
  x.vrbl = NULL,
  y.vrbl = NULL,
  d.x = NULL,
  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 ADL estimates

x.vrbl

named vector of the x variables and corresponding lag orders in the ADL model

y.vrbl

named vector of the (lagged) y variables and corresponding lag orders in the ADL model

d.x

the order of differencing of the x variable in the ADL model

d.y

the order of differencing of the y variable in the ADL 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? (For y variables where d.y is 0, this is automatically levels.) The default is levels

inferences.x

does the user want to apply the shock history to the independent variable in levels or in differences? (For x variables where d.x is 0, this is automatically levels.) The default is levels

dM.level

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

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

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

Author

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

Examples

# ADL(1,1)
# Use the toy data to run an ADL. No argument is made this is well specified; it is just expository 
model.toydata <- lm(y ~ l_1_y + x + l_1_x, data = toy.ts.interaction.data)

# Pulse effect of x
GDRF.adl.plot(model = model.toydata, 
        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)


# Step effect of x. You can store the data to draw your own plot,
#  if you prefer
test.cumulative <- GDRF.adl.plot(model = model.toydata, 
        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)
test.cumulative$plot
#> NULL