statsmodels.tsa.vector_ar.irf.IRAnalysis.err_band_sz2#

IRAnalysis.err_band_sz2(orth=False, svar=False, repl=1000, signif=0.05, rng=None, burn=100, component=None)[source]#

IRF Sims-Zha error band method 2

This method does not assume symmetric error bands around mean.

Parameters:

orthbool, default False: Compute orthogonalized impulse responses
svarbool, default False: Use structural IRFs
replint, default 1000: Number of MC replications
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
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, default 100: Number of initial simulated obs to discard
componentneqs x neqs array, default to largest for each: Index of column of eigenvector/value to use for each error band Note: period of impulse (t=0) is not included when computing principal component

References

Sims, Christopher A., and Tao Zha. 1999. “Error Bands for Impulse Response”. Econometrica 67: 1113-1155.