irf: Impulse Response Function
In BGVAR: Bayesian Global Vector Autoregressions

irf	R Documentation

Impulse Response Function

Description

This function calculates three alternative ways of dynamic responses, namely generalized impulse response functions (GIRFs) as in Pesaran and Shin (1998), orthogonalized impulse response functions using a Cholesky decomposition and finally impulse response functions given a set of user-specified sign restrictions.

Usage

irf(x, n.ahead=24, shockinfo=NULL, quantiles=NULL, 
    expert=NULL, verbose=TRUE)

Arguments

`x`	Object of class `bgvar`.
`n.ahead`	Forecasting horizon.
`shockinfo`	Dataframe with additional information about the nature of shocks. Depending on the `ident` argument, the dataframe has to be specified differently. In order to get a dummy version for each identification scheme use `get_shockinfo`.
`quantiles`	Numeric vector with posterior quantiles. Default is set to compute median along with 68%/80%/90% confidence intervals.
`expert`	Expert settings, must be provided as list. Default is set to `NULL`. `MaxTries` Numeric specifying maximal number of tries for finding a rotation matrix with sign-restrictions. Attention: setting this number very large may results in very long computational times. Default is set to `MaxTries=100`. `save.store` If set to `TRUE` the full posterior of both, impulses responses and rotation matrices, are returned. Default is set to `FALSE` in order to save storage. `use_R` Boolean whether IRF computation should fall back on `R` version, otherwise `Rcpp` version is used. `applyfun` In case `use_R=TRUE`, this allows for user-specific apply function, which has to have the same interface than `lapply`. If `cores=NULL` then `lapply` is used, if set to a numeric either `parallel::parLapply()` is used on Windows platforms and `parallel::mclapply()` on non-Windows platforms. `cores` Numeric specifying the number of cores which should be used, also `all` and `half` is possible. By default only one core is used.
`verbose`	If set to `FALSE` it suppresses printing messages to the console.

Value

Returns a list of class bgvar.irf with the following elements:

posterior

Four-dimensional array (K times n.ahead times number of shocks times Q) that contains Q quantiles of the posterior distribution of the impulse response functions.

shockinfo

Dataframe with details on identification specification.

rot.nr

In case identification is based on sign restrictions (i.e., ident="sign"), this provides the number of rotation matrices found for the number of posterior draws (save*save_thin).

struc.obj

List object that contains posterior quantitites needed when calculating historical decomposition and structural errors via hd.decomp.

A: Median posterior of global coefficient matrix.
Ginv: Median posterior of matrix Ginv, which describes contemporaneous relationships between countries.
S: Posterior median of matrix with country variance-covariance matrices on the main diagonal.
Rmed: Posterior rotation matrix if ident="sign".

model.obj

List object that contains model-specific information, in particular

xglobal: Data of the model.
lags: Lag specification of the model.

IRF_store

Four-dimensional array (K times n.ahead times number of shock times draws) which stores the whole posterior distribution. Exists only if save.store=TRUE.

R_store

Three-dimensional array (K times K times draws) which stores all rotation matrices. Exists only if save.store=TRUE.

Author(s)

Maximilian Boeck, Martin Feldkircher, Florian Huber

References

Arias, J.E., Rubio-Ramirez, J.F, and D.F. Waggoner (2018) Inference Based on SVARs Identified with Sign and Zero Restrictions: Theory and Applications. Econometrica Vol. 86(2), pp. 685-720.

D'Amico, S. and T. B. King (2017) What Does Anticipated Monetary Policy Do? Federal Reserve Bank of Chicago Working paper series, Nr. 2015-10.

Pesaran, H.M. and Y. Shin (1998) Generalized impulse response analysis in linear multivariate models. Economics Letters, Volume 58, Issue 1, p. 17-29.

Examples


oldpar <- par(no.readonly = TRUE)
# First example, a US monetary policy shock, quarterly data
library(BGVAR)
data(testdata)
# US monetary policy shock
model.eer<-bgvar(Data=testdata, W=W.test, draws=50, burnin=50, 
                 plag=1, prior="SSVS", eigen=TRUE)

# generalized impulse responses
shockinfo<-get_shockinfo("girf")
shockinfo$shock<-"US.stir"; shockinfo$scale<--100

irf.girf.us.mp<-irf(model.eer, n.ahead=24, shockinfo=shockinfo)

# cholesky identification
shockinfo<-get_shockinfo("chol")
shockinfo$shock<-"US.stir"; shockinfo$scale<--100

irf.chol.us.mp<-irf(model.eer, n.ahead=24, shockinfo=shockinfo)

# sign restrictions
shockinfo <- get_shockinfo("sign")
shockinfo <- add_shockinfo(shockinfo, shock="US.stir", restriction=c("US.y","US.Dp"), 
                           sign=c("<","<"), horizon=c(1,1), scale=1, prob=1)
irf.sign.us.mp<-irf(model.eer, n.ahead=24, shockinfo=shockinfo)

# sign restrictions
shockinfo <- get_shockinfo("sign")
shockinfo <- add_shockinfo(shockinfo, shock="US.stir", restriction=c("US.y","US.Dp"), 
sign=c("<","<"), horizon=c(1,1), scale=1, prob=1)
irf.sign.us.mp<-irf(model.eer, n.ahead=24, shockinfo=shockinfo)

#' # sign restrictions with relaxed cross-country restrictions
shockinfo <- get_shockinfo("sign")
# restriction for other countries holds to 75\%
shockinfo <- add_shockinfo(shockinfo, shock="US.stir", restriction=c("US.y","EA.y","UK.y"), 
                           sign=c("<","<","<"), horizon=1, scale=1, prob=c(1,0.75,0.75))
shockinfo <- add_shockinfo(shockinfo, shock="US.stir", restriction=c("US.Dp","EA.Dp","UK.Dp"),
                           sign=c("<","<","<"), horizon=1, scale=1, prob=c(1,0.75,0.75))
irf.sign.us.mp<-irf(model.eer, n.ahead=20, shockinfo=shockinfo)

BGVAR documentation built on Sept. 30, 2024, 5:06 p.m.