Recursive BVAR with a flat Normal inverted-Wishart prior

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Description

Bayesian vector autoregression (BVAR) model with a flat Normal inverted-Wishart prior. Shocks are identified using a Cholesky decomposition.

Usage

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rfbvar(Y=NULL, nlags=4, draws=1000, constant=TRUE, steps=24, shock=1)

Arguments

Y

A ts object containing the data series used for estimation; this should be of size T x nvar.

nlags

The number of lags to include of each variable. The default value is 4.

draws

An integer value for the number of Markov Chain Monte Carlo (MCMC) sampling replications. The default value is 1000.

constant

A logical statement on whether to include an intercept in the model. The default is 'TRUE'.

steps

An integer value for the horizon of the impulse response calculations. The default value is 24.

shock

An integer value specifiying for which shock the impulse responses are calculated. The number corresponds to the variable ordering in Y. The default value is 1.

Details

This function estimates a BVAR model using a flat Normal inverted-Wishart prior. Shocks are identified using a Cholesky decomposition. The ordering in the decomposition corresponds to the ordering of the variables in Y. The size of the shock is one standard deviation. Posterior draws are only returned for the variable specified in shock. In order to get the remaining impulse responses, re-run the model for different values of shock (see example below).

Value

A list of the posterior draws, which contains:

IRFS

A draws x steps x nvar array of posterior impulse responses.

FEVDS

A draws x steps x nvar array of posterior forecast error variance decompositions.

SHOCKS

A draws x (T-nlags) array of posterior draws of the model's shocks.

Bdraws

A draws x nvar array of posterior draws of the model coefficients.

Sdraws

A draws x nvar x nvar array of posterior draws of the model's variance-covariance matrix.

Note

Users should consult the reference papers and the package vignette for more information.

Author(s)

Christian Danne

References

Canova, F. (2007), Methods for Applied Macroeconomic Research, Princeton, NJ: Princeton University Press.

See Also

uhlig.reject, uhlig.penalty, rwz.reject, fp.target, irfplot, fevdplot.

Examples

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## Not run: 
# Replication of Figure 5 of Uhlig (2005)

set.seed(12345)
data(uhligdata)

# variable labels for plots
vl <- c("GDP","GDP Deflator","Comm.Pr.Index","Fed Funds Rate",
        "NB Reserves", "Total Reserves")

# FED funds rate shock
model0 <- rfbvar(Y=uhligdata, nlags=12, draws=1000, constant=FALSE,
                  steps=60, shock=4)

# plot impulse response functions
irfplot(irfdraws=model0$IRFS, type="median", labels=vl, save=FALSE, bands=c(0.16, 0.84),
        grid=TRUE, bw=FALSE)

## End(Not run)