statsmodels.tsa.vector_ar.irf.IRAnalysis.plot_cum_effects#
- IRAnalysis.plot_cum_effects(orth=False, *, impulse=None, response=None, signif=0.05, plot_params=None, figsize=(10, 10), subplot_params=None, plot_stderr=True, stderr_type='asym', repl=1000, rng=None, err_bands=None)#
Plot cumulative impulse response functions
- Parameters:
- orthbool,
defaultFalse Compute orthogonalized impulse responses
- impulse{
str,int} variable providing the impulse
- response{
str,int} variable affected by the impulse
- signif
float(0 <signif< 1) Significance level for error bars, defaults to 95% CI
- subplot_params
dict To pass to subplot plotting functions. Example: if fonts are too big, pass {‘fontsize’ : 8} or some number to your taste.
- plot_params
dict Keyword arguments to pass to the individual plotting functions.
- figsize(
float,float),default(10, 10) Figure size (width, height in inches)
- plot_stderrbool,
defaultTrue Plot standard impulse response error bands
- stderr_type
str ‘asym’: default, computes asymptotic standard errors ‘mc’: Monte Carlo standard errors (use repl)
- repl
int,default1000 Number of replications for Monte Carlo standard errors
- rng{
None,int, array_like[int],numpy.random.Generator,numpy.random.RandomState},optional np.random seed for Monte Carlo 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.
- err_bands
ndarrayofshape(2,periods+ 1,neqs,neqs),optional Pre-computed error bands. The first dimension contains the lower and upper bounds of the confidence interval, respectively. If provided, the internal calculation of standard errors is bypassed and
stderr_typeis used only for plot formatting.
- orthbool,