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",
  effect.type = "marginal",
  prediction.values = NULL,
  baseline.y = NULL,
  baseline.y.se = 0,
  shock.size = 1,
  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: a named numeric vector in which the names correspond to an independent variable and its lags and the numbers correspond to the specific lag order of each variable
y.vrbl: a named numeric vector in which the names correspond to lags of the dependent variable and the numbers correspond to the specific lag order of each variable. Can be NULL if the model has no lagged dependent variables
d.x: an integer describing how many times the independent variable was differenced before model estimation
d.y: an integer describing how many times the dependent variable was differenced before model estimation
shock.history: the desired shock history. shock.history determines the shock history (h) (which can be expressed as an integer) that will be applied to the independent variable. -1 represents a pulse (Impulse Response Function). 0 represents a step (Step Response Function). 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
effect.type: whether to return marginal effects or fitted values. marginal returns the GDRF as a marginal effect. fitted returns the GDRF as a fitted value, relative to a baseline value of y. The default is marginal
prediction.values: a named list of values for non-y variables in the model, used to calculate a steady-state baseline when effect.type = "fitted" and d.y = 0 and baseline.y is not supplied. This allows for the calculation of model-based uncertainty. If any differenced variables are included in the model, they should be set to 0. Ignored when d.y > 0
baseline.y: a user-supplied baseline value of y in levels. For d.y = 0, this overrides the steady-state calculation from prediction.values if provided. For d.y > 0 with inferences.y = "levels", this is required (otherwise it is just marginal effects). Only used when effect.type = "fitted"
baseline.y.se: a user-supplied standard error for the baseline value of y (to suggest uncertainty around predictions). If supplied, this is added in quadrature to the standard errors of the GDRF estimates. Only used when effect.type = "fitted" and inferences.y = "levels". The default is 0: in recognition that this is user-constructed uncertainty. Possible values would be the square root of the standard deviation of y (in levels)
shock.size: the size of the shock to x in the units of x. Only used when effect.type = "fitted"; marginal effects are not scaled. Defaults to 1 (a marginal effect)
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

Author

Soren Jordan, Garrett N. Vande Kamp, and Reshi 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

# Fitted values: steady state baseline from prediction.values
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",
  effect.type = "fitted",
  prediction.values = list("x" = 0, "l_1_x" = 0),
  s.limit = 20)
#> Warning: If any differenced variables are included in the model, ensure they are set to 0 in prediction.values for a meaningful steady-state prediction