def plot_cum_effects(self, orth=False, impulse=None, response=None,
signif=0.05, plot_params=None,
subplot_params=None, plot_stderr=True,
stderr_type='asym', repl=1000, seed=None):
"""
Plot cumulative impulse response functions
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
impulse : string or int
variable providing the impulse
response : string or 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 funcions. Example: if fonts are too big,
pass {'fontsize' : 8} or some number to your taste.
plot_params : dict
plot_stderr: bool, default True
Plot standard impulse response error bands
stderr_type: string
'asym': default, computes asymptotic standard errors
'mc': monte carlo standard errors (use rpl)
repl: int, default 1000
Number of replications for monte carlo standard errors
seed: int
np.random.seed for Monte Carlo replications
"""
if orth:
title = 'Cumulative responses responses (orthogonalized)'
cum_effects = self.orth_cum_effects
lr_effects = self.orth_lr_effects
else:
title = 'Cumulative responses'
cum_effects = self.cum_effects
lr_effects = self.lr_effects
if stderr_type not in ['asym', 'mc']:
raise TypeError
else:
if stderr_type == 'asym':
stderr = self.cum_effect_cov(orth=orth)
if stderr_type == 'mc':
stderr = self.cum_errband_mc(orth=orth, repl=repl,
signif=signif, seed=seed)
if not plot_stderr:
stderr = None
plotting.irf_grid_plot(cum_effects, stderr, impulse, response,
self.model.names, title, signif=signif,
hlines=lr_effects, subplot_params=subplot_params,
plot_params=plot_params, stderr_type=stderr_type)
def plot(self,
orth=False,
impulse=None,
response=None,
signif=0.05,
plot_params=None,
subplot_params=None,
plot_stderr=True,
stderr_type='asym',
repl=1000,
seed=None,
component=None):
"""
Plot impulse responses
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
impulse : string or int
variable providing the impulse
response : string or 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 funcions. Example: if fonts are too big,
pass {'fontsize' : 8} or some number to your taste.
plot_params : dict
plot_stderr: bool, default True
Plot standard impulse response error bands
stderr_type: string
'asym': default, computes asymptotic standard errors
'mc': monte carlo standard errors (use rpl)
repl: int, default 1000
Number of replications for Monte Carlo and Sims-Zha standard errors
seed: int
np.random.seed for Monte Carlo replications
component: array or vector of principal component indices
"""
periods = self.periods
model = self.model
svar = self.svar
if orth and svar:
raise ValueError("For SVAR system, set orth=False")
if orth:
title = 'Impulse responses (orthogonalized)'
irfs = self.orth_irfs
elif svar:
title = 'Impulse responses (structural)'
irfs = self.svar_irfs
else:
title = 'Impulse responses'
irfs = self.irfs
if plot_stderr == False:
stderr = None
elif stderr_type not in ['asym', 'mc', 'sz1', 'sz2', 'sz3']:
raise ValueError(
"Error type must be either 'asym', 'mc','sz1','sz2', or 'sz3'")
else:
if stderr_type == 'asym':
stderr = self.cov(orth=orth)
if stderr_type == 'mc':
stderr = self.errband_mc(orth=orth,
svar=svar,
repl=repl,
signif=signif,
seed=seed)
if stderr_type == 'sz1':
stderr = self.err_band_sz1(orth=orth,
svar=svar,
repl=repl,
signif=signif,
seed=seed,
component=component)
if stderr_type == 'sz2':
stderr = self.err_band_sz2(orth=orth,
svar=svar,
repl=repl,
signif=signif,
seed=seed,
component=component)
if stderr_type == 'sz3':
stderr = self.err_band_sz3(orth=orth,
svar=svar,
repl=repl,
signif=signif,
seed=seed,
component=component)
plotting.irf_grid_plot(irfs,
stderr,
impulse,
response,
self.model.names,
title,
signif=signif,
subplot_params=subplot_params,
plot_params=plot_params,
stderr_type=stderr_type)
def plot_cum_effects(self,
orth=False,
impulse=None,
response=None,
signif=0.05,
plot_params=None,
subplot_params=None,
plot_stderr=True,
stderr_type='asym',
repl=1000,
seed=None):
"""
Plot cumulative impulse response functions
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
impulse : string or int
variable providing the impulse
response : string or 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 funcions. Example: if fonts are too big,
pass {'fontsize' : 8} or some number to your taste.
plot_params : dict
plot_stderr: bool, default True
Plot standard impulse response error bands
stderr_type: string
'asym': default, computes asymptotic standard errors
'mc': monte carlo standard errors (use rpl)
repl: int, default 1000
Number of replications for monte carlo standard errors
seed: int
np.random.seed for Monte Carlo replications
"""
if orth:
title = 'Cumulative responses responses (orthogonalized)'
cum_effects = self.orth_cum_effects
lr_effects = self.orth_lr_effects
else:
title = 'Cumulative responses'
cum_effects = self.cum_effects
lr_effects = self.lr_effects
if stderr_type not in ['asym', 'mc']:
raise TypeError
else:
if stderr_type == 'asym':
stderr = self.cum_effect_cov(orth=orth)
if stderr_type == 'mc':
stderr = self.cum_errband_mc(orth=orth,
repl=repl,
signif=signif,
seed=seed)
if not plot_stderr:
stderr = None
plotting.irf_grid_plot(cum_effects,
stderr,
impulse,
response,
self.model.names,
title,
signif=signif,
hlines=lr_effects,
subplot_params=subplot_params,
plot_params=plot_params,
stderr_type=stderr_type)
def plot(self, orth=False, impulse=None, response=None,
signif=0.05, plot_params=None, subplot_params=None,
plot_stderr=True, stderr_type='asym', repl=1000,
seed=None, component=None):
"""
Plot impulse responses
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
impulse : string or int
variable providing the impulse
response : string or 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 funcions. Example: if fonts are too big,
pass {'fontsize' : 8} or some number to your taste.
plot_params : dict
plot_stderr: bool, default True
Plot standard impulse response error bands
stderr_type: string
'asym': default, computes asymptotic standard errors
'mc': monte carlo standard errors (use rpl)
repl: int, default 1000
Number of replications for Monte Carlo and Sims-Zha standard errors
seed: int
np.random.seed for Monte Carlo replications
component: array or vector of principal component indices
"""
periods = self.periods
model = self.model
svar = self.svar
if orth and svar:
raise ValueError("For SVAR system, set orth=False")
if orth:
title = 'Impulse responses (orthogonalized)'
irfs = self.orth_irfs
elif svar:
title = 'Impulse responses (structural)'
irfs = self.svar_irfs
else:
title = 'Impulse responses'
irfs = self.irfs
if plot_stderr == False:
stderr = None
elif stderr_type not in ['asym', 'mc', 'sz1', 'sz2','sz3']:
raise ValueError("Error type must be either 'asym', 'mc','sz1','sz2', or 'sz3'")
else:
if stderr_type == 'asym':
stderr = self.cov(orth=orth)
if stderr_type == 'mc':
stderr = self.errband_mc(orth=orth, svar=svar,
repl=repl, signif=signif,
seed=seed)
if stderr_type == 'sz1':
stderr = self.err_band_sz1(orth=orth, svar=svar,
repl=repl, signif=signif,
seed=seed,
component=component)
if stderr_type == 'sz2':
stderr = self.err_band_sz2(orth=orth, svar=svar,
repl=repl, signif=signif,
seed=seed,
component=component)
if stderr_type == 'sz3':
stderr = self.err_band_sz3(orth=orth, svar=svar,
repl=repl, signif=signif,
seed=seed,
component=component)
plotting.irf_grid_plot(irfs, stderr, impulse, response,
self.model.names, title, signif=signif,
subplot_params=subplot_params,
plot_params=plot_params, stderr_type=stderr_type)