irf.mvgam {mvgam} | R Documentation |
Calculate latent VAR impulse response functions
Description
Compute Generalized or Orthogonalized Impulse Response Functions (IRFs) from
mvgam
models with Vector Autoregressive dynamics
Usage
irf(object, ...)
## S3 method for class 'mvgam'
irf(object, h = 10, cumulative = FALSE, orthogonal = FALSE, ...)
Arguments
object |
|
... |
ignored |
h |
Positive |
cumulative |
|
orthogonal |
|
Details
Generalized or Orthogonalized Impulse Response Functions can be computed
using the posterior estimates of Vector Autoregressive parameters. This function
generates a positive "shock" for a target process at time t = 0
and then
calculates how each of the remaining processes in the latent VAR are expected
to respond over the forecast horizon h
. The function computes IRFs for all
processes in the object and returns them in an array that can be plotted using
the S3 plot
function. To inspect community-level metrics of stability using latent
VAR processes, you can use the related stability
function.
Value
An object of class mvgam_irf
containing the posterior IRFs. This
object can be used with the supplied S3 functions plot
Author(s)
Nicholas J Clark
References
PH Pesaran & Shin Yongcheol (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters 58: 17–29.
See Also
VAR
, plot.mvgam_irf
, stability
,
fevd
Examples
# Simulate some time series that follow a latent VAR(1) process
simdat <- sim_mvgam(
family = gaussian(),
n_series = 4,
trend_model = VAR(cor = TRUE),
prop_trend = 1
)
plot_mvgam_series(data = simdat$data_train, series = "all")
# Fit a model that uses a latent VAR(1)
mod <- mvgam(y ~ -1,
trend_formula = ~1,
trend_model = VAR(cor = TRUE),
family = gaussian(),
data = simdat$data_train,
chains = 2,
silent = 2
)
# Calulate Generalized IRFs for each series
irfs <- irf(mod, h = 12, cumulative = FALSE)
# Plot them
plot(irfs, series = 1)
plot(irfs, series = 2)
plot(irfs, series = 3)
# Calculate posterior median, upper and lower 90th quantiles
# of the impulse responses
summary(irfs)