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, default False

Compute orthogonalized impulse response error bands

replint

number of Monte Carlo replications to perform

stepsint, default 10

number of impulse response periods

signiffloat (0 < signif <1)

Significance level for error bars, defaults to 95% CI

rng{None, int, array_like[int], numpy.random.Generator, numpy.random.RandomState}, optional

np.random seed for replications

seed{None, int, array_like[int], numpy.random.Generator, 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

number of initial observations to discard for simulation

cumbool, default False

produce cumulative irf error bands

Returns:

Tuple of lower and upper arrays of ma_rep monte carlo standard errors

Notes

Lütkepohl (2005) Appendix D