Provides an overplotted, color-coded version of the MSBVAR IRFs plot.
This is an experimental function using color rather than the separate
plots produced in `plot.mc.irf`

1 2 3 |

`x` |
Output of the |

`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 |

`component` |
If using one of the eigendecomposition methods, the
eigenvector |

`probs` |
is the width of the error bands. Default
is |

`varnames` |
List of variable names of length |

`...` |
Other graphics parameters. |

This function plots the output of a Monte Carlo simulation of
MSBVAR 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. Here the plot colors the responses for each reqime, per the
R default color pallette for colors `1:h`

.

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. |

Patrick T. Brandt

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Not run:
data(IsraelPalestineConflict)
m1 <- msbvar(IsraelPalestineConflict, p=1, h=2, lambda0=0.6,
lambda1=0.1, lambda3=1, lambda4=0.5, lambda5=0,
mu5=0, mu6=0, qm=12, alpha.prior=matrix(10, 2, 2),
prior=0, max.iter=20)
m2p <- gibbs.msbvar(m1, N1=1000, N2=10000, permute=FALSE, Sigma.idx=1)
irf2 <- mc.irf(m2p, nsteps=12)
plot.ms.irf(irf2)
## End(Not run)
``` |

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