Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences in three forms: causal inferences from ADL models, traditional time series quantities of interest (short- and long-run effects), and dynamic conditional relationships.
Installation
tseffects is available on CRAN. To install the stable version released to CRAN, install as normal:
install.packages("tseffects")
library(tseffects)Usage: causal inferences
Drawing causal inferences from ADL and GECM models is easy. From an ADL(1,1), just specify the independent and dependent variables, as well as the treatment history desired.
# 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
return.data = TRUE
)