plot.mc.irf: Plotting posteriors of Monte Carlo simulated impulse...

In MSBVAR: Markov-Switching, Bayesian, Vector Autoregression Models

Description

Provides a plotting method for the mc.irf Monte Carlo sample of impulse responses. Responses can be plotted with classical or Bayesian error bands, as suggested by Sims and Zha (1999).

Usage

## S3 method for class 'mc.irf'
plot(x, method=c("Sims-Zha2"), component=1,
                      probs=c(0.16,0.84), varnames = attr(x, "eqnames"),
                      regimelabels=NULL, ask=TRUE, ...)

Arguments

x	Output of the mc.irf function
method	Method to be used for the error band construction. Default method is to use the eigendecomposition method proposed by Sims and Zha. Defined methods are "Percentile" (error bands are based on percentiles specified in probs), "Normal Approximation" (Gaussian approximation for interval of width probs), "Sims-Zha1" (Gaussian approximation with linear eigendecomposition), "Sims-Zha2" (Percentiles with eigendecomposition for each impulse response function), "Sims-Zha3" (Percentiles with eigendecomposition of the full stacked impulse responses)
component	If using one of the eigendecomposition methods, the eigenvector component to be used for the error band construction. Default is the first or largest eigenvector component.
probs	is the width of the error bands. Default is c(0.16, 0.84) which is a 68% band that is approximately one standard deviation, as suggested by Sims and Zha.
varnames	List of variable names of length m for labeling the impulse responses. Default are the input variable names from the relevent estimation method.
regimelabels	For MSBVAR models from mc.irf, a character vector of length h for the regime-specific IRFs. Default of NULL leads to automatic generation of "Regime 1", "Regime 2", etc.
ask	Default = TRUE, ask before showing the next regime's IRFs for MSBVAR models?
...	Other graphics parameters.

Details

This function plots the output of a Monte Carlo simulation of (MS)(B)(BS)VAR impulse response functions produced by mc.irf. The function allows the user to choose among a variety of frequentist (normal appproximation and percentile) and Bayesian (eigendecomposition) methods for constructing error bands around a set of impulse responses. Impulses or shocks are in the columns and the rows are the responses.

Value

The primary reason for this function is to plot impulse responses and their error bands. Secondarily, it returns an invisible list of the impulses responses, their error bands, and summary measures of the fractions of the variance in the eigenvector methods that explain the total variation of each response.

responses	Responses and their error bands
eigenvector.fractions	Fraction of the variation in each response that is explained by the chosen eigenvectors. NULL for non-eigenvector methods.

Author(s)

Patrick T. Brandt

References

Brandt, Patrick T. and John R. Freeman. 2006. "Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis" Political Analysis 14(1):1-36.

Sims, C.A. and Tao Zha. 1999. "Error Bands for Impulse Responses." Econometrica. 67(5): 1113-1156.

See Also

See Also mc.irf for the computation of Monte Carlo samples of impulse responses, szbsvar for estimation of the posterior moments of the B-SVAR model, gibbs.A0 for Gibbs sampling the posterior of the A(0) for the model, and

Examples

## Not run: 
data(IsraelPalestineConflict)
fit.BVAR <- szbvar(IsraelPalestineConflict, p=6, z=NULL, lambda0=0.6,
                   lambda1=0.1, lambda3=2, lambda4=0.5, lambda5=0,
                   mu5=0, mu6=0, nu=3, qm=4, prior=0,
                   posterior.fit=FALSE)

posterior.impulses <- mc.irf(fit.BVAR, nsteps=12, draws=1000)
plot(posterior.impulses, method = c("Percentile"))

## End(Not run)

Example output

##
## MSBVAR Package v.0.9-2
## Build date:  Thu Jul 20 09:22:58 2017 
## Copyright (C) 2005-2017, Patrick T. Brandt
## Written by Patrick T. Brandt
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0351179, SES-0351205, SES-0540816, and SES-0921051)
##

Monte Carlo IRF Iteration = 1000

MSBVAR documentation built on May 30, 2017, 1:23 a.m.