statsmodels.tsa.vector_ar.svar_model.SVARResults.irf_errband_mc

SVARResults.irf_errband_mc(orth=False, repl=1000, steps=10, signif=0.05, rng=None, burn=100, cum=False)

Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions

Parameters:

orthbool, optional

Compute orthogonalized impulse response error bands. The default is False.

replint, optional

number of Monte Carlo replications to perform. The default is 1000.

stepsint, optional

number of impulse response periods. The default is 10.

signiffloat, optional

Significance level for error bars, must be between 0 and 1, defaults to 95% CI (0.05).

rngint, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional

Source of random numbers used for the Monte Carlo replications. If rng is None, a new Generator is created using fresh entropy from the operating system. If rng is an int, a new RandomState instance is created, seeded with rng; this integer-seeding behavior is deprecated and will change to creating a Generator in a future release. If rng is already a Generator or RandomState instance, that instance is used.

seedint, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional

Deprecated since version 0.15: seed has been deprecated. In-line with SPEC-007, use rng for passing a random number generator or seed.

burnint, optional

number of initial observations to discard for simulation. The default is 100.

cumbool, optional

produce cumulative irf error bands. The default is False.

Returns:

ErrorBand

A result object with fields lower and upper, arrays of ma_rep Monte Carlo standard errors.

Notes

Lütkepohl (2005) Appendix D