"""Impulse response-related code"""
from statsmodels.compat.pandas import deprecate_kwarg
import numpy as np
import numpy.linalg as la
import scipy.linalg as L
from statsmodels.tools._decorators import cache_readonly
import statsmodels.tsa.tsatools as tsa
from statsmodels.tsa.vector_ar import plotting, util
mat = np.array
class BaseIRAnalysis:
"""
Base class for plotting and computing IRF-related statistics, designed
to handle both known and estimated processes
Parameters
----------
model : VAR, SVAR, or VECM instance
The fitted model used to compute impulse responses.
P : ndarray, optional
The matrix used for orthogonalization, satisfying sigma_u = P P'.
If None, computed as the Cholesky decomposition of the model's
residual covariance matrix.
periods : int, default 10
Number of periods to compute the impulse responses for.
order : sequence, optional
Alternate variable order for the Cholesky decomposition. Not
currently implemented.
svar : bool, default False
Flag indicating whether the model is a structural VAR.
vecm : bool, default False
Flag indicating whether the model is a VECM.
"""
def __init__(self, model, P=None, periods=10, order=None, svar=False, vecm=False):
self.model = model
self.periods = periods
self.neqs, self.lags, self.T = model.neqs, model.k_ar, model.nobs
self.order = order
if P is None:
sigma = model.sigma_u
# TODO, may be difficult at the moment
# if order is not None:
# indexer = [model.get_eq_index(name) for name in order]
# sigma = sigma[:, indexer][indexer, :]
# if sigma.shape != model.sigma_u.shape:
# raise ValueError('variable order is wrong length')
P = la.cholesky(sigma)
self.P = P
self.svar = svar
self.irfs = model.ma_rep(periods)
if svar:
self.svar_irfs = model.svar_ma_rep(periods, P=P)
else:
self.orth_irfs = model.orth_ma_rep(periods, P=P)
self.cum_effects = self.irfs.cumsum(axis=0)
if svar:
self.svar_cum_effects = self.svar_irfs.cumsum(axis=0)
else:
self.orth_cum_effects = self.orth_irfs.cumsum(axis=0)
# long-run effects may be infinite for VECMs.
if not vecm:
self.lr_effects = model.long_run_effects()
if svar:
self.svar_lr_effects = np.dot(model.long_run_effects(), P)
else:
self.orth_lr_effects = np.dot(model.long_run_effects(), P)
# auxiliary stuff
if vecm:
self._A = util.comp_matrix(model.var_rep)
else:
self._A = util.comp_matrix(model.coefs)
def _choose_irfs(self, orth=False, svar=False):
if orth:
return self.orth_irfs
elif svar:
return self.svar_irfs
else:
return self.irfs
def cov(self, *args, **kwargs):
raise NotImplementedError
def cum_effect_cov(self, *args, **kwargs):
raise NotImplementedError
@deprecate_kwarg("seed", "rng")
def plot(
self,
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,
component=None,
err_bands=None,
):
"""
Plot impulse responses
Parameters
----------
orth : bool, default False
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_stderr : bool, default True
Plot standard impulse response error bands
stderr_type : str
'asym': default, computes asymptotic standard errors
'mc': Monte Carlo standard errors (use repl)
repl : int, default 1000
Number of replications for Monte Carlo and Sims-Zha 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:: 0.15
seed has been deprecated. In-line with SPEC-007, use
rng for passing a random number generator or seed.
component : array or vector of principal component indices
err_bands : ndarray of shape (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_type`` is used only for plot formatting.
"""
svar = self.svar
if orth and svar:
raise ValueError("For SVAR system, set orth=False")
irfs = self._choose_irfs(orth, svar)
if orth:
title = "Impulse responses (orthogonalized)"
elif svar:
title = "Impulse responses (structural)"
else:
title = "Impulse responses"
if stderr_type not in ["asym", "mc", "sz1", "sz2", "sz3"]:
raise ValueError(
"Error type must be either 'asym', 'mc','sz1','sz2', or 'sz3'"
)
if plot_stderr is False:
stderr = None
elif err_bands is not None:
expected_shape = (2, self.periods + 1, self.neqs, self.neqs)
if np.asarray(err_bands).shape != expected_shape:
raise ValueError(
f"err_bands has shape {np.asarray(err_bands).shape}, expected "
f"{expected_shape} (2, periods+1, neqs, neqs)."
)
stderr = err_bands
elif stderr_type == "asym":
stderr = self.cov(orth=orth)
elif stderr_type == "mc":
stderr = self.errband_mc(
orth=orth, svar=svar, repl=repl, signif=signif, rng=rng
)
elif stderr_type == "sz1":
stderr = self.err_band_sz1(
orth=orth,
svar=svar,
repl=repl,
signif=signif,
rng=rng,
component=component,
)
elif stderr_type == "sz2":
stderr = self.err_band_sz2(
orth=orth,
svar=svar,
repl=repl,
signif=signif,
rng=rng,
component=component,
)
else: # stderr_type == "sz3":
stderr = self.err_band_sz3(
orth=orth,
svar=svar,
repl=repl,
signif=signif,
rng=rng,
component=component,
)
fig = plotting.irf_grid_plot(
irfs,
stderr,
impulse,
response,
self.model.names,
title,
signif=signif,
subplot_params=subplot_params,
plot_params=plot_params,
figsize=figsize,
stderr_type=stderr_type,
)
return fig
@deprecate_kwarg("seed", "rng")
def plot_cum_effects(
self,
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
----------
orth : bool, default False
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_stderr : bool, default True
Plot standard impulse response error bands
stderr_type : str
'asym': default, computes asymptotic standard errors
'mc': Monte Carlo standard errors (use repl)
repl : int, default 1000
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:: 0.15
seed has been deprecated. In-line with SPEC-007, use
rng for passing a random number generator or seed.
err_bands : ndarray of shape (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_type`` is used only for plot formatting.
"""
if orth:
title = "Cumulative 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 not plot_stderr:
stderr = None
elif err_bands is not None:
expected_shape = (2, self.periods + 1, self.neqs, self.neqs)
if np.asarray(err_bands).shape != expected_shape:
raise ValueError(
f"err_bands has shape {np.asarray(err_bands).shape}, expected "
f"{expected_shape} (2, periods+1, neqs, neqs)."
)
stderr = err_bands
elif stderr_type not in ["asym", "mc"]:
raise ValueError("`stderr_type` must be one of 'asym', 'mc'")
elif stderr_type == "asym":
stderr = self.cum_effect_cov(orth=orth)
else: # stderr_type == "mc"
stderr = self.cum_errband_mc(orth=orth, repl=repl, signif=signif, rng=rng)
fig = 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,
figsize=figsize,
stderr_type=stderr_type,
)
return fig
[docs]
class IRAnalysis(BaseIRAnalysis):
"""
Impulse response analysis class. Computes impulse responses, asymptotic
standard errors, and produces relevant plots
Parameters
----------
model : VAR, SVAR, or VECM instance
The fitted model used to compute impulse responses.
P : ndarray, optional
The matrix used for orthogonalization, satisfying sigma_u = P P'.
If None, computed as the Cholesky decomposition of the model's
residual covariance matrix.
periods : int, default 10
Number of periods to compute the impulse responses for.
order : sequence, optional
Alternate variable order for the Cholesky decomposition. Not
currently implemented.
svar : bool, default False
Flag indicating whether the model is a structural VAR.
vecm : bool, default False
Flag indicating whether the model is a VECM.
Notes
-----
Using Lütkepohl (2005) notation
"""
def __init__(self, model, P=None, periods=10, order=None, svar=False, vecm=False):
BaseIRAnalysis.__init__(
self, model, P=P, periods=periods, order=order, svar=svar, vecm=vecm
)
if vecm:
self.cov_a = model.cov_var_repr
else:
self.cov_a = model._cov_alpha
self.cov_sig = model._cov_sigma
# memoize dict for G matrix function
self._g_memo = {}
[docs]
def cov(self, orth=False):
"""
Compute asymptotic standard errors for impulse response coefficients
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
Returns
-------
ndarray
Array of shape (periods + 1, neqs ** 2, neqs ** 2) containing
the covariance matrix of the impulse response coefficients for
each period.
Notes
-----
Lütkepohl eq 3.7.5
"""
if orth:
return self._orth_cov()
covs = self._empty_covm(self.periods + 1)
covs[0] = np.zeros((self.neqs**2, self.neqs**2))
for i in range(1, self.periods + 1):
Gi = self.G[i - 1]
covs[i] = Gi @ self.cov_a @ Gi.T
return covs
[docs]
@deprecate_kwarg("seed", "rng")
def errband_mc(
self, orth=False, svar=False, repl=1000, signif=0.05, rng=None, burn=100
):
"""
IRF Monte Carlo integrated error bands
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
svar : bool, default False
Compute structural impulse responses
repl : int, default 1000
Number of MC replications
signif : float (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:: 0.15
seed has been deprecated. In-line with SPEC-007, use
rng for passing a random number generator or seed.
burn : int, default 100
Number of initial simulated obs to discard
"""
model = self.model
periods = self.periods
if svar:
return model.sirf_errband_mc(
orth=orth,
repl=repl,
steps=periods,
signif=signif,
rng=rng,
burn=burn,
cum=False,
)
else:
return model.irf_errband_mc(
orth=orth,
repl=repl,
steps=periods,
signif=signif,
rng=rng,
burn=burn,
cum=False,
)
[docs]
@deprecate_kwarg("seed", "rng")
def err_band_sz1(
self,
orth=False,
svar=False,
repl=1000,
signif=0.05,
rng=None,
burn=100,
component=None,
):
"""
IRF Sims-Zha error band method 1. Assumes symmetric error bands around
mean.
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
svar : bool, default False
Use structural IRFs
repl : int, default 1000
Number of MC replications
signif : float (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:: 0.15
seed has been deprecated. In-line with SPEC-007, use
rng for passing a random number generator or seed.
burn : int, default 100
Number of initial simulated obs to discard
component : neqs 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.
"""
model = self.model
periods = self.periods
irfs = self._choose_irfs(orth, svar)
neqs = self.neqs
irf_resim = model.irf_resim(
orth=orth, repl=repl, steps=periods, rng=rng, burn=burn
)
q = util.norm_signif_level(signif)
W, eigva, k = self._eigval_decomp_SZ(irf_resim)
if component is not None:
if np.shape(component) != (neqs, neqs):
raise ValueError(
"Component array must be " + str(neqs) + " x " + str(neqs)
)
if np.argmax(component) >= neqs * periods:
raise ValueError("Atleast one of the components does not exist")
else:
k = component
# here take the kth column of W, which we determine by finding the largest eigenvalue of the covaraince matrix
lower = np.copy(irfs)
upper = np.copy(irfs)
for i in range(neqs):
for j in range(neqs):
lower[1:, i, j] = irfs[1:, i, j] + W[i, j, :, k[i, j]] * q * np.sqrt(
eigva[i, j, k[i, j]]
)
upper[1:, i, j] = irfs[1:, i, j] - W[i, j, :, k[i, j]] * q * np.sqrt(
eigva[i, j, k[i, j]]
)
return lower, upper
[docs]
@deprecate_kwarg("seed", "rng")
def err_band_sz2(
self,
orth=False,
svar=False,
repl=1000,
signif=0.05,
rng=None,
burn=100,
component=None,
):
"""
IRF Sims-Zha error band method 2
This method does not assume symmetric error bands around mean.
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
svar : bool, default False
Use structural IRFs
repl : int, default 1000
Number of MC replications
signif : float (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:: 0.15
seed has been deprecated. In-line with SPEC-007, use
rng for passing a random number generator or seed.
burn : int, default 100
Number of initial simulated obs to discard
component : neqs 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.
"""
model = self.model
periods = self.periods
irfs = self._choose_irfs(orth, svar)
neqs = self.neqs
irf_resim = model.irf_resim(
orth=orth, repl=repl, steps=periods, rng=rng, burn=100
)
W, eigva, k = self._eigval_decomp_SZ(irf_resim)
if component is not None:
if np.shape(component) != (neqs, neqs):
raise ValueError(
"Component array must be " + str(neqs) + " x " + str(neqs)
)
if np.argmax(component) >= neqs * periods:
raise ValueError("Atleast one of the components does not exist")
else:
k = component
gamma = np.zeros((repl, periods + 1, neqs, neqs))
for p in range(repl):
for i in range(neqs):
for j in range(neqs):
gamma[p, 1:, i, j] = W[i, j, k[i, j], :] * irf_resim[p, 1:, i, j]
gamma_sort = np.sort(gamma, axis=0) # sort to get quantiles
indx = round(signif / 2 * repl) - 1, round((1 - signif / 2) * repl) - 1
lower = np.copy(irfs)
upper = np.copy(irfs)
for i in range(neqs):
for j in range(neqs):
lower[:, i, j] = irfs[:, i, j] + gamma_sort[indx[0], :, i, j]
upper[:, i, j] = irfs[:, i, j] + gamma_sort[indx[1], :, i, j]
return lower, upper
[docs]
@deprecate_kwarg("seed", "rng")
def err_band_sz3(
self,
orth=False,
svar=False,
repl=1000,
signif=0.05,
rng=None,
burn=100,
component=None,
):
"""
IRF Sims-Zha error band method 3. Does not assume symmetric error
bands around mean.
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
svar : bool, default False
Use structural IRFs
repl : int, default 1000
Number of MC replications
signif : float (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:: 0.15
seed has been deprecated. In-line with SPEC-007, use
rng for passing a random number generator or seed.
burn : int, default 100
Number of initial simulated obs to discard
component : vector length neqs, 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.
"""
model = self.model
periods = self.periods
irfs = self._choose_irfs(orth, svar)
neqs = self.neqs
irf_resim = model.irf_resim(
orth=orth, repl=repl, steps=periods, rng=rng, burn=100
)
stack = np.zeros((neqs, repl, periods * neqs))
# stack left to right, up and down
for p in range(repl):
for i in range(neqs):
stack[i, p, :] = np.ravel(irf_resim[p, 1:, :, i].T)
stack_cov = np.zeros((neqs, periods * neqs, periods * neqs))
W = np.zeros((neqs, periods * neqs, periods * neqs))
eigva = np.zeros((neqs, periods * neqs))
k = np.zeros(neqs, dtype=int)
if component is not None:
if np.size(component) != (neqs):
raise ValueError("Component array must be of length " + str(neqs))
if np.argmax(component) >= neqs * periods:
raise ValueError("Atleast one of the components does not exist")
else:
k = component
# compute for eigen decomp for each stack
for i in range(neqs):
stack_cov[i] = np.cov(stack[i], rowvar=0)
W[i], eigva[i], k[i] = util.eigval_decomp(stack_cov[i])
gamma = np.zeros((repl, periods + 1, neqs, neqs))
for p in range(repl):
for j in range(neqs):
for i in range(neqs):
gamma[p, 1:, i, j] = (
W[j, k[j], i * periods : (i + 1) * periods]
* irf_resim[p, 1:, i, j]
)
if i == neqs - 1:
gamma[p, 1:, i, j] = (
W[j, k[j], i * periods :] * irf_resim[p, 1:, i, j]
)
gamma_sort = np.sort(gamma, axis=0) # sort to get quantiles
indx = round(signif / 2 * repl) - 1, round((1 - signif / 2) * repl) - 1
lower = np.copy(irfs)
upper = np.copy(irfs)
for i in range(neqs):
for j in range(neqs):
lower[:, i, j] = irfs[:, i, j] + gamma_sort[indx[0], :, i, j]
upper[:, i, j] = irfs[:, i, j] + gamma_sort[indx[1], :, i, j]
return lower, upper
def _eigval_decomp_SZ(self, irf_resim):
"""
Eigenvalue decomposition of the covariance matrix of the resimulated
impulse responses
Parameters
----------
irf_resim : ndarray
Monte Carlo resimulated impulse responses.
Returns
-------
W : ndarray
Array of eigenvectors.
eigva : ndarray
Array of eigenvalues.
k : ndarray
Matrix indicating the column number of the largest eigenvalue
for each c_i,j.
"""
neqs = self.neqs
periods = self.periods
cov_hold = np.zeros((neqs, neqs, periods, periods))
for i in range(neqs):
for j in range(neqs):
cov_hold[i, j, :, :] = np.cov(irf_resim[:, 1:, i, j], rowvar=0)
W = np.zeros((neqs, neqs, periods, periods))
eigva = np.zeros((neqs, neqs, periods, 1))
k = np.zeros((neqs, neqs), dtype=int)
for i in range(neqs):
for j in range(neqs):
W[i, j, :, :], eigva[i, j, :, 0], k[i, j] = util.eigval_decomp(
cov_hold[i, j, :, :]
)
return W, eigva, k
[docs]
@cache_readonly
def G(self):
# Gi matrices as defined on p. 111
K = self.neqs
# nlags = self.model.p
# J = np.hstack((np.eye(K),) + (np.zeros((K, K)),) * (nlags - 1))
def _make_g(i):
# p. 111 Lutkepohl
G = 0.0
for m in range(i):
# be a bit cute to go faster
idx = i - 1 - m
if idx in self._g_memo:
apow = self._g_memo[idx]
else:
apow = la.matrix_power(self._A.T, idx)
# apow = np.dot(J, apow)
apow = apow[:K]
self._g_memo[idx] = apow
# take first K rows
piece = np.kron(apow, self.irfs[m])
G = G + piece
return G
return [_make_g(i) for i in range(1, self.periods + 1)]
def _orth_cov(self):
# Lutkepohl 3.7.8
Ik = np.eye(self.neqs)
PIk = np.kron(self.P.T, Ik)
H = self.H
covs = self._empty_covm(self.periods + 1)
for i in range(self.periods + 1):
if i == 0:
apiece = 0
else:
Ci = np.dot(PIk, self.G[i - 1])
apiece = Ci @ self.cov_a @ Ci.T
Cibar = np.dot(np.kron(Ik, self.irfs[i]), H)
bpiece = (Cibar @ self.cov_sig @ Cibar.T) / self.T
# Lutkepohl typo, cov_sig correct
covs[i] = apiece + bpiece
return covs
[docs]
def cum_effect_cov(self, orth=False):
"""
Compute asymptotic standard errors for cumulative impulse response
coefficients
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
Returns
-------
ndarray
Array of shape (periods + 1, neqs ** 2, neqs ** 2) containing
the covariance matrix of the cumulative impulse response
coefficients for each period.
Notes
-----
eq. 3.7.7 (non-orth), 3.7.10 (orth)
"""
Ik = np.eye(self.neqs)
PIk = np.kron(self.P.T, Ik)
F = 0.0
covs = self._empty_covm(self.periods + 1)
for i in range(self.periods + 1):
if i > 0:
F = F + self.G[i - 1]
if orth:
if i == 0:
apiece = 0
else:
Bn = np.dot(PIk, F)
apiece = Bn @ self.cov_a @ Bn.T
Bnbar = np.dot(np.kron(Ik, self.cum_effects[i]), self.H)
bpiece = (Bnbar @ self.cov_sig @ Bnbar.T) / self.T
covs[i] = apiece + bpiece
else:
if i == 0:
covs[i] = np.zeros((self.neqs**2, self.neqs**2))
continue
covs[i] = F @ self.cov_a @ F.T
return covs
[docs]
@deprecate_kwarg("seed", "rng")
def cum_errband_mc(self, orth=False, repl=1000, signif=0.05, rng=None, burn=100):
"""
IRF Monte Carlo integrated error bands of cumulative effect
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
svar : bool, default False
Use structural IRFs
repl : int, default 1000
Number of MC replications
signif : float (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:: 0.15
seed has been deprecated. In-line with SPEC-007, use
rng for passing a random number generator or seed.
burn : int, default 100
Number of initial simulated obs to discard
"""
model = self.model
periods = self.periods
return model.irf_errband_mc(
orth=orth,
repl=repl,
steps=periods,
signif=signif,
rng=rng,
burn=burn,
cum=True,
)
[docs]
def lr_effect_cov(self, orth=False):
"""
Compute asymptotic standard errors for long-run effects
Parameters
----------
orth : bool, default False
Compute orthogonalized impulse responses
Returns
-------
ndarray
The covariance matrix of the long-run effects.
"""
lre = self.lr_effects
Finfty = np.kron(np.tile(lre.T, self.lags), lre)
Ik = np.eye(self.neqs)
if orth:
Binf = np.dot(np.kron(self.P.T, np.eye(self.neqs)), Finfty)
Binfbar = np.dot(np.kron(Ik, lre), self.H)
return Binf @ self.cov_a @ Binf.T + Binfbar @ self.cov_sig @ Binfbar.T
else:
return Finfty @ self.cov_a @ Finfty.T
[docs]
def stderr(self, orth=False):
return np.array([tsa.unvec(np.sqrt(np.diag(c))) for c in self.cov(orth=orth)])
[docs]
def cum_effect_stderr(self, orth=False):
return np.array(
[tsa.unvec(np.sqrt(np.diag(c))) for c in self.cum_effect_cov(orth=orth)]
)
[docs]
def lr_effect_stderr(self, orth=False):
cov = self.lr_effect_cov(orth=orth)
return tsa.unvec(np.sqrt(np.diag(cov)))
def _empty_covm(self, periods):
return np.zeros((periods, self.neqs**2, self.neqs**2), dtype=float)
[docs]
@cache_readonly
def H(self):
k = self.neqs
Lk = tsa.elimination_matrix(k)
Kkk = tsa.commutation_matrix(k, k)
Ik = np.eye(k)
# B = Lk @ (np.eye(k**2) + commutation_matrix(k, k)) @ \
# np.kron(self.P, np.eye(k)) @ Lk.T
# return Lk.T @ L.inv(B)
B = Lk @ (np.kron(Ik, self.P) @ Kkk + np.kron(self.P, Ik)) @ Lk.T
return np.dot(Lk.T, L.inv(B))
[docs]
def fevd_table(self):
raise NotImplementedError
