tseffects (version 0.2.1)

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

Description

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

Usage

GDTE.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,
  s.limit = 20,
  se.type = "const",
  return.data = FALSE,
  return.plot = TRUE,
  return.formulae = FALSE,
  ...
)

Value

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

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 (h) 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

s.limit

an integer for the number of periods to determine the GDTE (beginning at s = 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

Author

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

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

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 <- GDTE.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",
                                  s.limit = 20, 
                                  return.plot = TRUE, 
                                  return.formulae = TRUE)
names(test.pulse)

# 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 <- GDTE.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",
                                  s.limit = 10, 
                                  return.plot = TRUE, 
                                  return.formulae = TRUE)