irf.mvgam: Calculate latent VAR impulse response functions

In mvgam: Multivariate (Dynamic) Generalized Additive Models

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 = 1, cumulative = FALSE, orthogonal = FALSE, ...)

Arguments

object	list object of class mvgam resulting from a call to mvgam() that used a Vector Autoregressive latent process model (either as VAR(cor = FALSE) or VAR(cor = TRUE))
...	ignored
h	Positive integer specifying the forecast horizon over which to calculate the IRF
cumulative	Logical flag indicating whether the IRF should be cumulative
orthogonal	Logical flag indicating whether orthogonalized IRFs should be calculated. Note that the order of the variables matters when calculating these

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

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

See Also

VAR, plot.mvgam_irf

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,
             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)

mvgam documentation built on Sept. 11, 2024, 8:55 p.m.