View source: R/64_irf_method.R
irf.bvar | R Documentation |
Retrieves / calculates impulse response functions (IRFs) and/or forecast
error variance decompositions (FEVDs) for Bayesian VARs generated via
bvar
. If the object is already present and no settings are
supplied it is simply retrieved, otherwise it will be calculated ex-post.
Note that FEVDs require the presence / calculation of IRFs.
To store the results you may want to assign the output using the setter
function (irf(x) <- irf(x)
). May also be used to update
confidence bands.
## S3 method for class 'bvar'
irf(x, ..., conf_bands, n_thin = 1L, verbose = FALSE)
## S3 method for class 'bvar'
fevd(x, ..., conf_bands, n_thin = 1L)
irf(x, ...)
## Default S3 method:
irf(x, ...)
irf(x) <- value
fevd(x, ...)
## Default S3 method:
fevd(x, ...)
fevd(x) <- value
## S3 method for class 'bvar_irf'
summary(object, vars_impulse = NULL, vars_response = NULL, ...)
x , object |
A |
... |
A |
conf_bands |
Numeric vector of confidence bands to apply.
E.g. for bands at 5%, 10%, 90% and 95% set this to |
n_thin |
Integer scalar. Every n_thin'th draw in x is used to calculate, others are dropped. |
verbose |
Logical scalar. Whether to print intermediate results and progress. |
value |
A |
vars_impulse , vars_response |
Optional numeric or character vector.
Used to subset the summary method's outputs to certain variables by position
or name (must be available). Defaults to |
Returns a list of class bvar_irf
including IRFs and optionally
FEVDs at desired confidence bands. The fevd
method only returns a
the nested bvar_fevd
object.
The summary method returns a numeric array of impulse responses at the
specified confidence bands.
plot.bvar_irf
; bv_irf
# Access a subset of the fred_qd dataset
data <- fred_qd[, c("CPIAUCSL", "UNRATE", "FEDFUNDS")]
# Transform it to be stationary
data <- fred_transform(data, codes = c(5, 5, 1), lag = 4)
# Estimate a BVAR using one lag, default settings and very few draws
x <- bvar(data, lags = 1, n_draw = 600L, n_burn = 100L, verbose = FALSE)
# Compute + store IRF with a longer horizon, no identification and thinning
irf(x) <- irf(x, bv_irf(horizon = 24L, identification = FALSE), n_thin = 5L)
# Update the confidence bands of the IRFs
irf(x, conf_bands = c(0.01, 0.05, 0.1))
# Recalculate with sign restrictions provided via the ellipsis
irf(x, sign_restr = matrix(c(1, NA, NA, -1, 1, -1, -1, 1, 1), nrow = 3))
# Recalculate with zero and sign restrictions provided via the ellipsis
irf(x, sign_restr = matrix(c(1, 0, 1, NA, 1, 1, -1, -1, 1), nrow = 3))
# Calculate the forecast error variance decomposition
fevd(x)
# Get a summary of the saved impulse response function
summary(x)
# Limit the summary to responses of variable #2
summary(x, vars_response = 2L)
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