Evaluate (and possibly plot) the General Dynamic Response Function (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",
  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 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.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 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
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
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

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)
# 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)
#> [1] "plot"      "formulae"  "binomials"