Evaluate (and possibly plot) the General Dynamic Treatment Effect (GDTE) for an autoregressive distributed lag (ADL) model

Usage

ts.ci.adl.plot(
  model = NULL,
  x.vrbl = NULL,
  y.vrbl = NULL,
  d.x = NULL,
  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 ADL estimates
x.vrbl: a named vector of the x variables and corresponding lag orders in the ADL model
y.vrbl: a 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
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? (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 counterfactual treatment 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: 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 ADL 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 ADL 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

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 <- lm(y ~ l_1_y + x + l_1_x, data = toy.ts.interaction.data)
test.pulse <- ts.ci.adl.plot(model = model,
                                  x.vrbl = c("x" = 0, "l_1_x" = 1), 
                                  y.vrbl = c("l_1_y" = 1),
                                  d.x = 0, 
                                  d.y = 0,
                                  te.type = "pulse", 
                                  inferences.y = "levels", 
                                  inferences.x = "levels",
                                  h.limit = 20, 
                                  return.plot = TRUE, 
                                  return.formulae = TRUE)
names(test.pulse)
#> [1] "plot"      "formulae"  "binomials"

# Using Cavari's (2019) approval model (without interactions)
# Cavari's original model: APPROVE ~ APPROVE_ECONOMY + APPROVE_FOREIGN + 
#     APPROVE_L1 + PARTY_IN + PARTY_OUT + UNRATE + 
#       MIP_MACROECONOMICS + MIP_FOREIGN + 
#     DIVIDEDGOV + ELECTION + HONEYMOON + as.factor(PRESIDENT)

cavari.model <- lm(APPROVE ~ APPROVE_ECONOMY + APPROVE_FOREIGN + MIP_MACROECONOMICS + MIP_FOREIGN +
     APPROVE_L1 + PARTY_IN + PARTY_OUT + UNRATE + 
     DIVIDEDGOV + ELECTION + HONEYMOON + as.factor(PRESIDENT), data = approval)

# What if there was a permanent, one-unit change in the salience of foreign affairs?
cavari.step <- ts.ci.adl.plot(model = cavari.model,
                                  x.vrbl = c("MIP_FOREIGN" = 0), 
                                  y.vrbl = c("APPROVE_L1" = 1),
                                  d.x = 0,
                                  d.y = 0,
                                  te.type = "ste", 
                                  inferences.y = "levels", 
                                  inferences.x = "levels",
                                  h.limit = 10, 
                                  return.plot = TRUE, 
                                  return.formulae = TRUE)
#> Warning: Variable names containing . replaced with _