"""
Vector Autoregression (VAR) processes
References
----------
Lutkepohl (2005) New Introduction to Multiple Time Series Analysis
"""
from __future__ import division, print_function
from statsmodels.compat.python import (range, lrange, string_types, StringIO, iteritems,
cStringIO)
from collections import defaultdict
import numpy as np
import numpy.linalg as npl
from numpy.linalg import cholesky as chol, solve
import scipy.stats as stats
import scipy.linalg as L
from statsmodels.tools.decorators import cache_readonly
from statsmodels.tools.tools import chain_dot
from statsmodels.tools.linalg import logdet_symm
from statsmodels.tsa.tsatools import vec, unvec
from statsmodels.tsa.vector_ar.irf import IRAnalysis
from statsmodels.tsa.vector_ar.output import VARSummary
import statsmodels.tsa.tsatools as tsa
import statsmodels.tsa.vector_ar.output as output
import statsmodels.tsa.vector_ar.plotting as plotting
import statsmodels.tsa.vector_ar.util as util
import statsmodels.tsa.base.tsa_model as tsbase
import statsmodels.base.wrapper as wrap
mat = np.array
#-------------------------------------------------------------------------------
# VAR process routines
def ma_rep(coefs, maxn=10):
r"""
MA(\infty) representation of VAR(p) process
Parameters
----------
coefs : ndarray (p x k x k)
maxn : int
Number of MA matrices to compute
Notes
-----
VAR(p) process as
.. math:: y_t = A_1 y_{t-1} + \ldots + A_p y_{t-p} + u_t
can be equivalently represented as
.. math:: y_t = \mu + \sum_{i=0}^\infty \Phi_i u_{t-i}
e.g. can recursively compute the \Phi_i matrices with \Phi_0 = I_k
Returns
-------
phis : ndarray (maxn + 1 x k x k)
"""
p, k, k = coefs.shape
phis = np.zeros((maxn+1, k, k))
phis[0] = np.eye(k)
# recursively compute Phi matrices
for i in range(1, maxn + 1):
for j in range(1, i+1):
if j > p:
break
phis[i] += np.dot(phis[i-j], coefs[j-1])
return phis
def is_stable(coefs, verbose=False):
"""
Determine stability of VAR(p) system by examining the eigenvalues of the
VAR(1) representation
Parameters
----------
coefs : ndarray (p x k x k)
Returns
-------
is_stable : bool
"""
A_var1 = util.comp_matrix(coefs)
eigs = np.linalg.eigvals(A_var1)
if verbose:
print('Eigenvalues of VAR(1) rep')
for val in np.abs(eigs):
print(val)
return (np.abs(eigs) <= 1).all()
def var_acf(coefs, sig_u, nlags=None):
"""
Compute autocovariance function ACF_y(h) up to nlags of stable VAR(p)
process
Parameters
----------
coefs : ndarray (p x k x k)
Coefficient matrices A_i
sig_u : ndarray (k x k)
Covariance of white noise process u_t
nlags : int, optional
Defaults to order p of system
Notes
-----
Ref: Lutkepohl p.28-29
Returns
-------
acf : ndarray, (p, k, k)
"""
p, k, _ = coefs.shape
if nlags is None:
nlags = p
# p x k x k, ACF for lags 0, ..., p-1
result = np.zeros((nlags + 1, k, k))
result[:p] = _var_acf(coefs, sig_u)
# yule-walker equations
for h in range(p, nlags + 1):
# compute ACF for lag=h
# G(h) = A_1 G(h-1) + ... + A_p G(h-p)
for j in range(p):
result[h] += np.dot(coefs[j], result[h-j-1])
return result
def _var_acf(coefs, sig_u):
"""
Compute autocovariance function ACF_y(h) for h=1,...,p
Notes
-----
Lutkepohl (2005) p.29
"""
p, k, k2 = coefs.shape
assert(k == k2)
A = util.comp_matrix(coefs)
# construct VAR(1) noise covariance
SigU = np.zeros((k*p, k*p))
SigU[:k,:k] = sig_u
# vec(ACF) = (I_(kp)^2 - kron(A, A))^-1 vec(Sigma_U)
vecACF = L.solve(np.eye((k*p)**2) - np.kron(A, A), vec(SigU))
acf = unvec(vecACF)
acf = acf[:k].T.reshape((p, k, k))
return acf
def forecast(y, coefs, intercept, steps):
"""
Produce linear MSE forecast
Parameters
----------
y :
coefs :
intercept :
steps :
Returns
-------
forecasts : ndarray (steps x neqs)
Notes
-----
Lutkepohl p. 37
Also used by DynamicVAR class
"""
p = len(coefs)
k = len(coefs[0])
# initial value
forcs = np.zeros((steps, k)) + intercept
# h=0 forecast should be latest observation
# forcs[0] = y[-1]
# make indices easier to think about
for h in range(1, steps + 1):
# y_t(h) = intercept + sum_1^p A_i y_t_(h-i)
f = forcs[h - 1]
for i in range(1, p + 1):
# slightly hackish
if h - i <= 0:
# e.g. when h=1, h-1 = 0, which is y[-1]
prior_y = y[h - i - 1]
else:
# e.g. when h=2, h-1=1, which is forcs[0]
prior_y = forcs[h - i - 1]
# i=1 is coefs[0]
f = f + np.dot(coefs[i - 1], prior_y)
forcs[h - 1] = f
return forcs
def forecast_cov(ma_coefs, sig_u, steps):
"""
Compute theoretical forecast error variance matrices
Parameters
----------
Returns
-------
forc_covs : ndarray (steps x neqs x neqs)
"""
k = len(sig_u)
forc_covs = np.zeros((steps, k, k))
prior = np.zeros((k, k))
for h in range(steps):
# Sigma(h) = Sigma(h-1) + Phi Sig_u Phi'
phi = ma_coefs[h]
var = chain_dot(phi, sig_u, phi.T)
forc_covs[h] = prior = prior + var
return forc_covs
def var_loglike(resid, omega, nobs):
r"""
Returns the value of the VAR(p) log-likelihood.
Parameters
----------
resid : ndarray (T x K)
omega : ndarray
Sigma hat matrix. Each element i,j is the average product of the
OLS residual for variable i and the OLS residual for variable j or
np.dot(resid.T,resid)/nobs. There should be no correction for the
degrees of freedom.
nobs : int
Returns
-------
llf : float
The value of the loglikelihood function for a VAR(p) model
Notes
-----
The loglikelihood function for the VAR(p) is
.. math::
-\left(\frac{T}{2}\right)
\left(\ln\left|\Omega\right|-K\ln\left(2\pi\right)-K\right)
"""
logdet = logdet_symm(np.asarray(omega))
neqs = len(omega)
part1 = - (nobs * neqs / 2) * np.log(2 * np.pi)
part2 = - (nobs / 2) * (logdet + neqs)
return part1 + part2
def _reordered(self, order):
#Create new arrays to hold rearranged results from .fit()
endog = self.endog
endog_lagged = self.endog_lagged
params = self.params
sigma_u = self.sigma_u
names = self.names
k_ar = self.k_ar
endog_new = np.zeros([np.size(endog,0),np.size(endog,1)])
endog_lagged_new = np.zeros([np.size(endog_lagged,0), np.size(endog_lagged,1)])
params_new_inc, params_new = [np.zeros([np.size(params,0), np.size(params,1)])
for i in range(2)]
sigma_u_new_inc, sigma_u_new = [np.zeros([np.size(sigma_u,0), np.size(sigma_u,1)])
for i in range(2)]
num_end = len(self.params[0])
names_new = []
#Rearrange elements and fill in new arrays
k = self.k_trend
for i, c in enumerate(order):
endog_new[:,i] = self.endog[:,c]
if k > 0:
params_new_inc[0,i] = params[0,i]
endog_lagged_new[:,0] = endog_lagged[:,0]
for j in range(k_ar):
params_new_inc[i+j*num_end+k,:] = self.params[c+j*num_end+k,:]
endog_lagged_new[:,i+j*num_end+k] = endog_lagged[:,c+j*num_end+k]
sigma_u_new_inc[i,:] = sigma_u[c,:]
names_new.append(names[c])
for i, c in enumerate(order):
params_new[:,i] = params_new_inc[:,c]
sigma_u_new[:,i] = sigma_u_new_inc[:,c]
return VARResults(endog=endog_new, endog_lagged=endog_lagged_new,
params=params_new, sigma_u=sigma_u_new,
lag_order=self.k_ar, model=self.model,
trend='c', names=names_new, dates=self.dates)
#-------------------------------------------------------------------------------
# VARProcess class: for known or unknown VAR process
[docs]class VAR(tsbase.TimeSeriesModel):
r"""
Fit VAR(p) process and do lag order selection
.. math:: y_t = A_1 y_{t-1} + \ldots + A_p y_{t-p} + u_t
Parameters
----------
endog : array-like
2-d endogenous response variable. The independent variable.
dates : array-like
must match number of rows of endog
References
----------
Lutkepohl (2005) New Introduction to Multiple Time Series Analysis
"""
def __init__(self, endog, dates=None, freq=None, missing='none'):
super(VAR, self).__init__(endog, None, dates, freq, missing=missing)
if self.endog.ndim == 1:
raise ValueError("Only gave one variable to VAR")
self.y = self.endog #keep alias for now
self.neqs = self.endog.shape[1]
def _get_predict_start(self, start, k_ar):
if start is None:
start = k_ar
return super(VAR, self)._get_predict_start(start)
[docs] def predict(self, params, start=None, end=None, lags=1, trend='c'):
"""
Returns in-sample predictions or forecasts
"""
start = self._get_predict_start(start, lags)
end, out_of_sample = self._get_predict_end(end)
if end < start:
raise ValueError("end is before start")
if end == start + out_of_sample:
return np.array([])
k_trend = util.get_trendorder(trend)
k = self.neqs
k_ar = lags
predictedvalues = np.zeros((end + 1 - start + out_of_sample, k))
if k_trend != 0:
intercept = params[:k_trend]
predictedvalues += intercept
y = self.y
X = util.get_var_endog(y, lags, trend=trend, has_constant='raise')
fittedvalues = np.dot(X, params)
fv_start = start - k_ar
pv_end = min(len(predictedvalues), len(fittedvalues) - fv_start)
fv_end = min(len(fittedvalues), end-k_ar+1)
predictedvalues[:pv_end] = fittedvalues[fv_start:fv_end]
if not out_of_sample:
return predictedvalues
# fit out of sample
y = y[-k_ar:]
coefs = params[k_trend:].reshape((k_ar, k, k)).swapaxes(1,2)
predictedvalues[pv_end:] = forecast(y, coefs, intercept, out_of_sample)
return predictedvalues
[docs] def fit(self, maxlags=None, method='ols', ic=None, trend='c',
verbose=False):
"""
Fit the VAR model
Parameters
----------
maxlags : int
Maximum number of lags to check for order selection, defaults to
12 * (nobs/100.)**(1./4), see select_order function
method : {'ols'}
Estimation method to use
ic : {'aic', 'fpe', 'hqic', 'bic', None}
Information criterion to use for VAR order selection.
aic : Akaike
fpe : Final prediction error
hqic : Hannan-Quinn
bic : Bayesian a.k.a. Schwarz
verbose : bool, default False
Print order selection output to the screen
trend, str {"c", "ct", "ctt", "nc"}
"c" - add constant
"ct" - constant and trend
"ctt" - constant, linear and quadratic trend
"nc" - co constant, no trend
Note that these are prepended to the columns of the dataset.
Notes
-----
Lutkepohl pp. 146-153
Returns
-------
est : VARResults
"""
lags = maxlags
if ic is not None:
selections = self.select_order(maxlags=maxlags, verbose=verbose)
if ic not in selections:
raise Exception("%s not recognized, must be among %s"
% (ic, sorted(selections)))
lags = selections[ic]
if verbose:
print('Using %d based on %s criterion' % (lags, ic))
else:
if lags is None:
lags = 1
k_trend = util.get_trendorder(trend)
self.exog_names = util.make_lag_names(self.endog_names, lags, k_trend)
self.nobs = len(self.endog) - lags
return self._estimate_var(lags, trend=trend)
def _estimate_var(self, lags, offset=0, trend='c'):
"""
lags : int
offset : int
Periods to drop from beginning-- for order selection so it's an
apples-to-apples comparison
trend : string or None
As per above
"""
# have to do this again because select_order doesn't call fit
self.k_trend = k_trend = util.get_trendorder(trend)
if offset < 0: # pragma: no cover
raise ValueError('offset must be >= 0')
y = self.y[offset:]
z = util.get_var_endog(y, lags, trend=trend, has_constant='raise')
y_sample = y[lags:]
# Lutkepohl p75, about 5x faster than stated formula
params = np.linalg.lstsq(z, y_sample)[0]
resid = y_sample - np.dot(z, params)
# Unbiased estimate of covariance matrix $\Sigma_u$ of the white noise
# process $u$
# equivalent definition
# .. math:: \frac{1}{T - Kp - 1} Y^\prime (I_T - Z (Z^\prime Z)^{-1}
# Z^\prime) Y
# Ref: Lutkepohl p.75
# df_resid right now is T - Kp - 1, which is a suggested correction
avobs = len(y_sample)
df_resid = avobs - (self.neqs * lags + k_trend)
sse = np.dot(resid.T, resid)
omega = sse / df_resid
varfit = VARResults(y, z, params, omega, lags, names=self.endog_names,
trend=trend, dates=self.data.dates, model=self)
return VARResultsWrapper(varfit)
[docs] def select_order(self, maxlags=None, verbose=True):
"""
Compute lag order selections based on each of the available information
criteria
Parameters
----------
maxlags : int
if None, defaults to 12 * (nobs/100.)**(1./4)
verbose : bool, default True
If True, print table of info criteria and selected orders
Returns
-------
selections : dict {info_crit -> selected_order}
"""
if maxlags is None:
maxlags = int(round(12*(len(self.endog)/100.)**(1/4.)))
ics = defaultdict(list)
for p in range(maxlags + 1):
# exclude some periods to same amount of data used for each lag
# order
result = self._estimate_var(p, offset=maxlags-p)
for k, v in iteritems(result.info_criteria):
ics[k].append(v)
selected_orders = dict((k, mat(v).argmin())
for k, v in iteritems(ics))
if verbose:
output.print_ic_table(ics, selected_orders)
return selected_orders
[docs]class VARProcess(object):
"""
Class represents a known VAR(p) process
Parameters
----------
coefs : ndarray (p x k x k)
intercept : ndarray (length k)
sigma_u : ndarray (k x k)
names : sequence (length k)
Returns
-------
**Attributes**:
"""
def __init__(self, coefs, intercept, sigma_u, names=None):
self.k_ar = len(coefs)
self.neqs = coefs.shape[1]
self.coefs = coefs
self.intercept = intercept
self.sigma_u = sigma_u
self.names = names
[docs] def get_eq_index(self, name):
"Return integer position of requested equation name"
return util.get_index(self.names, name)
def __str__(self):
output = ('VAR(%d) process for %d-dimensional response y_t'
% (self.k_ar, self.neqs))
output += '\nstable: %s' % self.is_stable()
output += '\nmean: %s' % self.mean()
return output
[docs] def is_stable(self, verbose=False):
"""Determine stability based on model coefficients
Parameters
----------
verbose : bool
Print eigenvalues of the VAR(1) companion
Notes
-----
Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of
the companion matrix must lie outside the unit circle
"""
return is_stable(self.coefs, verbose=verbose)
[docs] def plotsim(self, steps=1000):
"""
Plot a simulation from the VAR(p) process for the desired number of
steps
"""
Y = util.varsim(self.coefs, self.intercept, self.sigma_u, steps=steps)
plotting.plot_mts(Y)
[docs] def mean(self):
r"""Mean of stable process
Lutkepohl eq. 2.1.23
.. math:: \mu = (I - A_1 - \dots - A_p)^{-1} \alpha
"""
return solve(self._char_mat, self.intercept)
[docs] def ma_rep(self, maxn=10):
r"""Compute MA(:math:`\infty`) coefficient matrices
Parameters
----------
maxn : int
Number of coefficient matrices to compute
Returns
-------
coefs : ndarray (maxn x k x k)
"""
return ma_rep(self.coefs, maxn=maxn)
[docs] def orth_ma_rep(self, maxn=10, P=None):
r"""Compute Orthogonalized MA coefficient matrices using P matrix such
that :math:`\Sigma_u = PP^\prime`. P defaults to the Cholesky
decomposition of :math:`\Sigma_u`
Parameters
----------
maxn : int
Number of coefficient matrices to compute
P : ndarray (k x k), optional
Matrix such that Sigma_u = PP', defaults to Cholesky descomp
Returns
-------
coefs : ndarray (maxn x k x k)
"""
if P is None:
P = self._chol_sigma_u
ma_mats = self.ma_rep(maxn=maxn)
return mat([np.dot(coefs, P) for coefs in ma_mats])
[docs] def long_run_effects(self):
"""Compute long-run effect of unit impulse
.. math::
\Psi_\infty = \sum_{i=0}^\infty \Phi_i
"""
return L.inv(self._char_mat)
@cache_readonly
def _chol_sigma_u(self):
return chol(self.sigma_u)
@cache_readonly
def _char_mat(self):
return np.eye(self.neqs) - self.coefs.sum(0)
[docs] def acf(self, nlags=None):
"""Compute theoretical autocovariance function
Returns
-------
acf : ndarray (p x k x k)
"""
return var_acf(self.coefs, self.sigma_u, nlags=nlags)
[docs] def acorr(self, nlags=None):
"""Compute theoretical autocorrelation function
Returns
-------
acorr : ndarray (p x k x k)
"""
return util.acf_to_acorr(self.acf(nlags=nlags))
[docs] def plot_acorr(self, nlags=10, linewidth=8):
"Plot theoretical autocorrelation function"
plotting.plot_full_acorr(self.acorr(nlags=nlags), linewidth=linewidth)
[docs] def forecast(self, y, steps):
"""Produce linear minimum MSE forecasts for desired number of steps
ahead, using prior values y
Parameters
----------
y : ndarray (p x k)
steps : int
Returns
-------
forecasts : ndarray (steps x neqs)
Notes
-----
Lutkepohl pp 37-38
"""
return forecast(y, self.coefs, self.intercept, steps)
[docs] def mse(self, steps):
"""
Compute theoretical forecast error variance matrices
Parameters
----------
steps : int
Number of steps ahead
Notes
-----
.. math:: \mathrm{MSE}(h) = \sum_{i=0}^{h-1} \Phi \Sigma_u \Phi^T
Returns
-------
forc_covs : ndarray (steps x neqs x neqs)
"""
ma_coefs = self.ma_rep(steps)
k = len(self.sigma_u)
forc_covs = np.zeros((steps, k, k))
prior = np.zeros((k, k))
for h in range(steps):
# Sigma(h) = Sigma(h-1) + Phi Sig_u Phi'
phi = ma_coefs[h]
var = chain_dot(phi, self.sigma_u, phi.T)
forc_covs[h] = prior = prior + var
return forc_covs
forecast_cov = mse
def _forecast_vars(self, steps):
covs = self.forecast_cov(steps)
# Take diagonal for each cov
inds = np.arange(self.neqs)
return covs[:, inds, inds]
[docs] def forecast_interval(self, y, steps, alpha=0.05):
"""Construct forecast interval estimates assuming the y are Gaussian
Parameters
----------
Notes
-----
Lutkepohl pp. 39-40
Returns
-------
(lower, mid, upper) : (ndarray, ndarray, ndarray)
"""
assert(0 < alpha < 1)
q = util.norm_signif_level(alpha)
point_forecast = self.forecast(y, steps)
sigma = np.sqrt(self._forecast_vars(steps))
forc_lower = point_forecast - q * sigma
forc_upper = point_forecast + q * sigma
return point_forecast, forc_lower, forc_upper
#-------------------------------------------------------------------------------
# VARResults class
[docs]class VARResults(VARProcess):
"""Estimate VAR(p) process with fixed number of lags
Parameters
----------
endog : array
endog_lagged : array
params : array
sigma_u : array
lag_order : int
model : VAR model instance
trend : str {'nc', 'c', 'ct'}
names : array-like
List of names of the endogenous variables in order of appearance in `endog`.
dates
Returns
-------
**Attributes**
aic
bic
bse
coefs : ndarray (p x K x K)
Estimated A_i matrices, A_i = coefs[i-1]
cov_params
dates
detomega
df_model : int
df_resid : int
endog
endog_lagged
fittedvalues
fpe
intercept
info_criteria
k_ar : int
k_trend : int
llf
model
names
neqs : int
Number of variables (equations)
nobs : int
n_totobs : int
params
k_ar : int
Order of VAR process
params : ndarray (Kp + 1) x K
A_i matrices and intercept in stacked form [int A_1 ... A_p]
pvalues
names : list
variables names
resid
roots : array
The roots of the VAR process are the solution to
(I - coefs[0]*z - coefs[1]*z**2 ... - coefs[p-1]*z**k_ar) = 0.
Note that the inverse roots are returned, and stability requires that
the roots lie outside the unit circle.
sigma_u : ndarray (K x K)
Estimate of white noise process variance Var[u_t]
sigma_u_mle
stderr
trenorder
tvalues
y :
ys_lagged
"""
_model_type = 'VAR'
def __init__(self, endog, endog_lagged, params, sigma_u, lag_order,
model=None, trend='c', names=None, dates=None):
self.model = model
self.y = self.endog = endog #keep alias for now
self.ys_lagged = self.endog_lagged = endog_lagged #keep alias for now
self.dates = dates
self.n_totobs, neqs = self.y.shape
self.nobs = self.n_totobs - lag_order
k_trend = util.get_trendorder(trend)
if k_trend > 0: # make this the polynomial trend order
trendorder = k_trend - 1
else:
trendorder = None
self.k_trend = k_trend
self.trendorder = trendorder
self.exog_names = util.make_lag_names(names, lag_order, k_trend)
self.params = params
# Initialize VARProcess parent class
# construct coefficient matrices
# Each matrix needs to be transposed
reshaped = self.params[self.k_trend:]
reshaped = reshaped.reshape((lag_order, neqs, neqs))
# Need to transpose each coefficient matrix
intercept = self.params[0]
coefs = reshaped.swapaxes(1, 2).copy()
super(VARResults, self).__init__(coefs, intercept, sigma_u, names=names)
[docs] def plot(self):
"""Plot input time series
"""
plotting.plot_mts(self.y, names=self.names, index=self.dates)
@property
def df_model(self):
"""Number of estimated parameters, including the intercept / trends
"""
return self.neqs * self.k_ar + self.k_trend
@property
def df_resid(self):
"Number of observations minus number of estimated parameters"
return self.nobs - self.df_model
@cache_readonly
[docs] def fittedvalues(self):
"""The predicted insample values of the response variables of the model.
"""
return np.dot(self.ys_lagged, self.params)
@cache_readonly
[docs] def resid(self):
"""Residuals of response variable resulting from estimated coefficients
"""
return self.y[self.k_ar:] - self.fittedvalues
[docs] def sample_acov(self, nlags=1):
return _compute_acov(self.y[self.k_ar:], nlags=nlags)
[docs] def sample_acorr(self, nlags=1):
acovs = self.sample_acov(nlags=nlags)
return _acovs_to_acorrs(acovs)
[docs] def plot_sample_acorr(self, nlags=10, linewidth=8):
"Plot theoretical autocorrelation function"
plotting.plot_full_acorr(self.sample_acorr(nlags=nlags),
linewidth=linewidth)
[docs] def resid_acov(self, nlags=1):
"""
Compute centered sample autocovariance (including lag 0)
Parameters
----------
nlags : int
Returns
-------
"""
return _compute_acov(self.resid, nlags=nlags)
[docs] def resid_acorr(self, nlags=1):
"""
Compute sample autocorrelation (including lag 0)
Parameters
----------
nlags : int
Returns
-------
"""
acovs = self.resid_acov(nlags=nlags)
return _acovs_to_acorrs(acovs)
@cache_readonly
[docs] def resid_corr(self):
"Centered residual correlation matrix"
return self.resid_acorr(0)[0]
@cache_readonly
[docs] def sigma_u_mle(self):
"""(Biased) maximum likelihood estimate of noise process covariance
"""
return self.sigma_u * self.df_resid / self.nobs
@cache_readonly
[docs] def cov_params(self):
"""Estimated variance-covariance of model coefficients
Notes
-----
Covariance of vec(B), where B is the matrix
[intercept, A_1, ..., A_p] (K x (Kp + 1))
Adjusted to be an unbiased estimator
Ref: Lutkepohl p.74-75
"""
z = self.ys_lagged
return np.kron(L.inv(np.dot(z.T, z)), self.sigma_u)
[docs] def cov_ybar(self):
r"""Asymptotically consistent estimate of covariance of the sample mean
.. math::
\sqrt(T) (\bar{y} - \mu) \rightarrow {\cal N}(0, \Sigma_{\bar{y}})\\
\Sigma_{\bar{y}} = B \Sigma_u B^\prime, \text{where } B = (I_K - A_1
- \cdots - A_p)^{-1}
Notes
-----
Lutkepohl Proposition 3.3
"""
Ainv = L.inv(np.eye(self.neqs) - self.coefs.sum(0))
return chain_dot(Ainv, self.sigma_u, Ainv.T)
#------------------------------------------------------------
# Estimation-related things
@cache_readonly
def _zz(self):
# Z'Z
return np.dot(self.ys_lagged.T, self.ys_lagged)
@property
def _cov_alpha(self):
"""
Estimated covariance matrix of model coefficients ex intercept
"""
# drop intercept
return self.cov_params[self.neqs:, self.neqs:]
@cache_readonly
def _cov_sigma(self):
"""
Estimated covariance matrix of vech(sigma_u)
"""
D_K = tsa.duplication_matrix(self.neqs)
D_Kinv = npl.pinv(D_K)
sigxsig = np.kron(self.sigma_u, self.sigma_u)
return 2 * chain_dot(D_Kinv, sigxsig, D_Kinv.T)
@cache_readonly
[docs] def llf(self):
"Compute VAR(p) loglikelihood"
return var_loglike(self.resid, self.sigma_u_mle, self.nobs)
@cache_readonly
[docs] def stderr(self):
"""Standard errors of coefficients, reshaped to match in size
"""
stderr = np.sqrt(np.diag(self.cov_params))
return stderr.reshape((self.df_model, self.neqs), order='C')
bse = stderr # statsmodels interface?
@cache_readonly
[docs] def tvalues(self):
"""Compute t-statistics. Use Student-t(T - Kp - 1) = t(df_resid) to test
significance.
"""
return self.params / self.stderr
@cache_readonly
[docs] def pvalues(self):
"""Two-sided p-values for model coefficients from Student t-distribution
"""
return stats.t.sf(np.abs(self.tvalues), self.df_resid)*2
[docs] def plot_forecast(self, steps, alpha=0.05, plot_stderr=True):
"""
Plot forecast
"""
mid, lower, upper = self.forecast_interval(self.y[-self.k_ar:], steps,
alpha=alpha)
plotting.plot_var_forc(self.y, mid, lower, upper, names=self.names,
plot_stderr=plot_stderr)
# Forecast error covariance functions
[docs] def forecast_cov(self, steps=1):
r"""Compute forecast covariance matrices for desired number of steps
Parameters
----------
steps : int
Notes
-----
.. math:: \Sigma_{\hat y}(h) = \Sigma_y(h) + \Omega(h) / T
Ref: Lutkepohl pp. 96-97
Returns
-------
covs : ndarray (steps x k x k)
"""
mse = self.mse(steps)
omegas = self._omega_forc_cov(steps)
return mse + omegas / self.nobs
#Monte Carlo irf standard errors
[docs] def irf_errband_mc(self, orth=False, repl=1000, T=10,
signif=0.05, seed=None, burn=100, cum=False):
"""
Compute Monte Carlo integrated error bands assuming normally
distributed for impulse response functions
Parameters
----------
orth: bool, default False
Compute orthoganalized impulse response error bands
repl: int
number of Monte Carlo replications to perform
T: int, default 10
number of impulse response periods
signif: float (0 < signif <1)
Significance level for error bars, defaults to 95% CI
seed: int
np.random.seed for replications
burn: int
number of initial observations to discard for simulation
cum: bool, default False
produce cumulative irf error bands
Notes
-----
Lutkepohl (2005) Appendix D
Returns
-------
Tuple of lower and upper arrays of ma_rep monte carlo standard errors
"""
neqs = self.neqs
mean = self.mean()
k_ar = self.k_ar
coefs = self.coefs
sigma_u = self.sigma_u
intercept = self.intercept
df_model = self.df_model
nobs = self.nobs
ma_coll = np.zeros((repl, T+1, neqs, neqs))
if (orth == True and cum == True):
fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\
orth_ma_rep(maxn=T).cumsum(axis=0)
elif (orth == True and cum == False):
fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\
orth_ma_rep(maxn=T)
elif (orth == False and cum == True):
fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\
ma_rep(maxn=T).cumsum(axis=0)
elif (orth == False and cum == False):
fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\
ma_rep(maxn=T)
for i in range(repl):
#discard first hundred to eliminate correct for starting bias
sim = util.varsim(coefs, intercept, sigma_u, steps=nobs+burn)
sim = sim[burn:]
ma_coll[i,:,:,:] = fill_coll(sim)
ma_sort = np.sort(ma_coll, axis=0) #sort to get quantiles
index = round(signif/2*repl)-1,round((1-signif/2)*repl)-1
lower = ma_sort[index[0],:, :, :]
upper = ma_sort[index[1],:, :, :]
return lower, upper
[docs] def irf_resim(self, orth=False, repl=1000, T=10,
seed=None, burn=100, cum=False):
"""
Simulates impulse response function, returning an array of simulations.
Used for Sims-Zha error band calculation.
Parameters
----------
orth: bool, default False
Compute orthoganalized impulse response error bands
repl: int
number of Monte Carlo replications to perform
T: int, default 10
number of impulse response periods
signif: float (0 < signif <1)
Significance level for error bars, defaults to 95% CI
seed: int
np.random.seed for replications
burn: int
number of initial observations to discard for simulation
cum: bool, default False
produce cumulative irf error bands
Notes
-----
Sims, Christoper A., and Tao Zha. 1999. "Error Bands for Impulse Response." Econometrica 67: 1113-1155.
Returns
-------
Array of simulated impulse response functions
"""
neqs = self.neqs
mean = self.mean()
k_ar = self.k_ar
coefs = self.coefs
sigma_u = self.sigma_u
intercept = self.intercept
df_model = self.df_model
nobs = self.nobs
if seed is not None:
np.random.seed(seed=seed)
ma_coll = np.zeros((repl, T+1, neqs, neqs))
if (orth == True and cum == True):
fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\
orth_ma_rep(maxn=T).cumsum(axis=0)
elif (orth == True and cum == False):
fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\
orth_ma_rep(maxn=T)
elif (orth == False and cum == True):
fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\
ma_rep(maxn=T).cumsum(axis=0)
elif (orth == False and cum == False):
fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\
ma_rep(maxn=T)
for i in range(repl):
#discard first hundred to eliminate correct for starting bias
sim = util.varsim(coefs, intercept, sigma_u, steps=nobs+burn)
sim = sim[burn:]
ma_coll[i,:,:,:] = fill_coll(sim)
return ma_coll
def _omega_forc_cov(self, steps):
# Approximate MSE matrix \Omega(h) as defined in Lut p97
G = self._zz
Ginv = L.inv(G)
# memoize powers of B for speedup
# TODO: see if can memoize better
B = self._bmat_forc_cov()
_B = {}
def bpow(i):
if i not in _B:
_B[i] = np.linalg.matrix_power(B, i)
return _B[i]
phis = self.ma_rep(steps)
sig_u = self.sigma_u
omegas = np.zeros((steps, self.neqs, self.neqs))
for h in range(1, steps + 1):
if h == 1:
omegas[h-1] = self.df_model * self.sigma_u
continue
om = omegas[h-1]
for i in range(h):
for j in range(h):
Bi = bpow(h - 1 - i)
Bj = bpow(h - 1 - j)
mult = np.trace(chain_dot(Bi.T, Ginv, Bj, G))
om += mult * chain_dot(phis[i], sig_u, phis[j].T)
omegas[h-1] = om
return omegas
def _bmat_forc_cov(self):
# B as defined on p. 96 of Lut
upper = np.zeros((1, self.df_model))
upper[0,0] = 1
lower_dim = self.neqs * (self.k_ar - 1)
I = np.eye(lower_dim)
lower = np.column_stack((np.zeros((lower_dim, 1)), I,
np.zeros((lower_dim, self.neqs))))
return np.vstack((upper, self.params.T, lower))
[docs] def summary(self):
"""Compute console output summary of estimates
Returns
-------
summary : VARSummary
"""
return VARSummary(self)
[docs] def irf(self, periods=10, var_decomp=None, var_order=None):
"""Analyze impulse responses to shocks in system
Parameters
----------
periods : int
var_decomp : ndarray (k x k), lower triangular
Must satisfy Omega = P P', where P is the passed matrix. Defaults to
Cholesky decomposition of Omega
var_order : sequence
Alternate variable order for Cholesky decomposition
Returns
-------
irf : IRAnalysis
"""
if var_order is not None:
raise NotImplementedError('alternate variable order not implemented'
' (yet)')
return IRAnalysis(self, P=var_decomp, periods=periods)
[docs] def fevd(self, periods=10, var_decomp=None):
"""
Compute forecast error variance decomposition ("fevd")
Returns
-------
fevd : FEVD instance
"""
return FEVD(self, P=var_decomp, periods=periods)
[docs] def reorder(self, order):
"""Reorder variables for structural specification
"""
if len(order) != len(self.params[0,:]):
raise ValueError("Reorder specification length should match number of endogenous variables")
#This convert order to list of integers if given as strings
if isinstance(order[0], string_types):
order_new = []
for i, nam in enumerate(order):
order_new.append(self.names.index(order[i]))
order = order_new
return _reordered(self, order)
#-------------------------------------------------------------------------------
# VAR Diagnostics: Granger-causality, whiteness of residuals, normality, etc.
[docs] def test_causality(self, equation, variables, kind='f', signif=0.05,
verbose=True):
"""Compute test statistic for null hypothesis of Granger-noncausality,
general function to test joint Granger-causality of multiple variables
Parameters
----------
equation : string or int
Equation to test for causality
variables : sequence (of strings or ints)
List, tuple, etc. of variables to test for Granger-causality
kind : {'f', 'wald'}
Perform F-test or Wald (chi-sq) test
signif : float, default 5%
Significance level for computing critical values for test,
defaulting to standard 0.95 level
Notes
-----
Null hypothesis is that there is no Granger-causality for the indicated
variables. The degrees of freedom in the F-test are based on the
number of variables in the VAR system, that is, degrees of freedom
are equal to the number of equations in the VAR times degree of freedom
of a single equation.
Returns
-------
results : dict
"""
if isinstance(variables, (string_types, int, np.integer)):
variables = [variables]
k, p = self.neqs, self.k_ar
# number of restrictions
N = len(variables) * self.k_ar
# Make restriction matrix
C = np.zeros((N, k ** 2 * p + k), dtype=float)
eq_index = self.get_eq_index(equation)
vinds = mat([self.get_eq_index(v) for v in variables])
# remember, vec is column order!
offsets = np.concatenate([k + k ** 2 * j + k * vinds + eq_index
for j in range(p)])
C[np.arange(N), offsets] = 1
# Lutkepohl 3.6.5
Cb = np.dot(C, vec(self.params.T))
middle = L.inv(chain_dot(C, self.cov_params, C.T))
# wald statistic
lam_wald = statistic = chain_dot(Cb, middle, Cb)
if kind.lower() == 'wald':
df = N
dist = stats.chi2(df)
elif kind.lower() == 'f':
statistic = lam_wald / N
df = (N, k * self.df_resid)
dist = stats.f(*df)
else:
raise Exception('kind %s not recognized' % kind)
pvalue = dist.sf(statistic)
crit_value = dist.ppf(1 - signif)
conclusion = 'fail to reject' if statistic < crit_value else 'reject'
results = {
'statistic' : statistic,
'crit_value' : crit_value,
'pvalue' : pvalue,
'df' : df,
'conclusion' : conclusion,
'signif' : signif
}
if verbose:
summ = output.causality_summary(results, variables, equation, kind)
print(summ)
return results
[docs] def test_whiteness(self, nlags=10, plot=True, linewidth=8):
"""
Test white noise assumption. Sample (Y) autocorrelations are compared
with the standard :math:`2 / \sqrt(T)` bounds.
Parameters
----------
plot : boolean, default True
Plot autocorrelations with 2 / sqrt(T) bounds
"""
acorrs = self.sample_acorr(nlags)
bound = 2 / np.sqrt(self.nobs)
# TODO: this probably needs some UI work
if (np.abs(acorrs) > bound).any():
print('FAIL: Some autocorrelations exceed %.4f bound. '
'See plot' % bound)
else:
print('PASS: No autocorrelations exceed %.4f bound' % bound)
if plot:
fig = plotting.plot_full_acorr(acorrs[1:],
xlabel=np.arange(1, nlags+1),
err_bound=bound,
linewidth=linewidth)
fig.suptitle(r"ACF plots with $2 / \sqrt{T}$ bounds "
"for testing whiteness assumption")
[docs] def test_normality(self, signif=0.05, verbose=True):
"""
Test assumption of normal-distributed errors using Jarque-Bera-style
omnibus Chi^2 test
Parameters
----------
signif : float
Test significance threshold
Notes
-----
H0 (null) : data are generated by a Gaussian-distributed process
"""
Pinv = npl.inv(self._chol_sigma_u)
w = np.array([np.dot(Pinv, u) for u in self.resid])
b1 = (w ** 3).sum(0) / self.nobs
lam_skew = self.nobs * np.dot(b1, b1) / 6
b2 = (w ** 4).sum(0) / self.nobs - 3
lam_kurt = self.nobs * np.dot(b2, b2) / 24
lam_omni = lam_skew + lam_kurt
omni_dist = stats.chi2(self.neqs * 2)
omni_pvalue = omni_dist.sf(lam_omni)
crit_omni = omni_dist.ppf(1 - signif)
conclusion = 'fail to reject' if lam_omni < crit_omni else 'reject'
results = {
'statistic' : lam_omni,
'crit_value' : crit_omni,
'pvalue' : omni_pvalue,
'df' : self.neqs * 2,
'conclusion' : conclusion,
'signif' : signif
}
if verbose:
summ = output.normality_summary(results)
print(summ)
return results
@cache_readonly
[docs] def detomega(self):
r"""
Return determinant of white noise covariance with degrees of freedom
correction:
.. math::
\hat \Omega = \frac{T}{T - Kp - 1} \hat \Omega_{\mathrm{MLE}}
"""
return L.det(self.sigma_u)
@cache_readonly
[docs] def info_criteria(self):
"information criteria for lagorder selection"
nobs = self.nobs
neqs = self.neqs
lag_order = self.k_ar
free_params = lag_order * neqs ** 2 + neqs * self.k_trend
ld = logdet_symm(self.sigma_u_mle)
# See Lutkepohl pp. 146-150
aic = ld + (2. / nobs) * free_params
bic = ld + (np.log(nobs) / nobs) * free_params
hqic = ld + (2. * np.log(np.log(nobs)) / nobs) * free_params
fpe = ((nobs + self.df_model) / self.df_resid) ** neqs * np.exp(ld)
return {
'aic' : aic,
'bic' : bic,
'hqic' : hqic,
'fpe' : fpe
}
@property
def aic(self):
"Akaike information criterion"
return self.info_criteria['aic']
@property
def fpe(self):
"""Final Prediction Error (FPE)
Lutkepohl p. 147, see info_criteria
"""
return self.info_criteria['fpe']
@property
def hqic(self):
"Hannan-Quinn criterion"
return self.info_criteria['hqic']
@property
def bic(self):
"Bayesian a.k.a. Schwarz info criterion"
return self.info_criteria['bic']
@cache_readonly
[docs] def roots(self):
neqs = self.neqs
k_ar = self.k_ar
p = neqs * k_ar
arr = np.zeros((p,p))
arr[:neqs,:] = np.column_stack(self.coefs)
arr[neqs:,:-neqs] = np.eye(p-neqs)
roots = np.linalg.eig(arr)[0]**-1
idx = np.argsort(np.abs(roots))[::-1] # sort by reverse modulus
return roots[idx]
class VARResultsWrapper(wrap.ResultsWrapper):
_attrs = {'bse' : 'columns_eq', 'cov_params' : 'cov',
'params' : 'columns_eq', 'pvalues' : 'columns_eq',
'tvalues' : 'columns_eq', 'sigma_u' : 'cov_eq',
'sigma_u_mle' : 'cov_eq',
'stderr' : 'columns_eq'}
_wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
_methods)
_wrap_methods.pop('cov_params') # not yet a method in VARResults
wrap.populate_wrapper(VARResultsWrapper, VARResults)
[docs]class FEVD(object):
"""
Compute and plot Forecast error variance decomposition and asymptotic
standard errors
"""
def __init__(self, model, P=None, periods=None):
self.periods = periods
self.model = model
self.neqs = model.neqs
self.names = model.model.endog_names
self.irfobj = model.irf(var_decomp=P, periods=periods)
self.orth_irfs = self.irfobj.orth_irfs
# cumulative impulse responses
irfs = (self.orth_irfs[:periods] ** 2).cumsum(axis=0)
rng = lrange(self.neqs)
mse = self.model.mse(periods)[:, rng, rng]
# lag x equation x component
fevd = np.empty_like(irfs)
for i in range(periods):
fevd[i] = (irfs[i].T / mse[i]).T
# switch to equation x lag x component
self.decomp = fevd.swapaxes(0, 1)
[docs] def summary(self):
buf = StringIO()
rng = lrange(self.periods)
for i in range(self.neqs):
ppm = output.pprint_matrix(self.decomp[i], rng, self.names)
buf.write('FEVD for %s\n' % self.names[i])
buf.write(ppm + '\n')
print(buf.getvalue())
[docs] def cov(self):
"""Compute asymptotic standard errors
Returns
-------
"""
raise NotImplementedError
[docs] def plot(self, periods=None, figsize=(10,10), **plot_kwds):
"""Plot graphical display of FEVD
Parameters
----------
periods : int, default None
Defaults to number originally specified. Can be at most that number
"""
import matplotlib.pyplot as plt
k = self.neqs
periods = periods or self.periods
fig, axes = plt.subplots(nrows=k, figsize=figsize)
fig.suptitle('Forecast error variance decomposition (FEVD)')
colors = [str(c) for c in np.arange(k, dtype=float) / k]
ticks = np.arange(periods)
limits = self.decomp.cumsum(2)
for i in range(k):
ax = axes[i]
this_limits = limits[i].T
handles = []
for j in range(k):
lower = this_limits[j - 1] if j > 0 else 0
upper = this_limits[j]
handle = ax.bar(ticks, upper - lower, bottom=lower,
color=colors[j], label=self.names[j],
**plot_kwds)
handles.append(handle)
ax.set_title(self.names[i])
# just use the last axis to get handles for plotting
handles, labels = ax.get_legend_handles_labels()
fig.legend(handles, labels, loc='upper right')
plotting.adjust_subplots(right=0.85)
#-------------------------------------------------------------------------------
def _compute_acov(x, nlags=1):
x = x - x.mean(0)
result = []
for lag in range(nlags + 1):
if lag > 0:
r = np.dot(x[lag:].T, x[:-lag])
else:
r = np.dot(x.T, x)
result.append(r)
return np.array(result) / len(x)
def _acovs_to_acorrs(acovs):
sd = np.sqrt(np.diag(acovs[0]))
return acovs / np.outer(sd, sd)
if __name__ == '__main__':
import statsmodels.api as sm
from statsmodels.tsa.vector_ar.util import parse_lutkepohl_data
import statsmodels.tools.data as data_util
np.set_printoptions(linewidth=140, precision=5)
sdata, dates = parse_lutkepohl_data('data/%s.dat' % 'e1')
names = sdata.dtype.names
data = data_util.struct_to_ndarray(sdata)
adj_data = np.diff(np.log(data), axis=0)
# est = VAR(adj_data, p=2, dates=dates[1:], names=names)
model = VAR(adj_data[:-16], dates=dates[1:-16], names=names)
# model = VAR(adj_data[:-16], dates=dates[1:-16], names=names)
est = model.fit(maxlags=2)
irf = est.irf()
y = est.y[-2:]
"""
# irf.plot_irf()
# i = 2; j = 1
# cv = irf.cum_effect_cov(orth=True)
# print np.sqrt(cv[:, j * 3 + i, j * 3 + i]) / 1e-2
# data = np.genfromtxt('Canada.csv', delimiter=',', names=True)
# data = data.view((float, 4))
"""
'''
mdata = sm.datasets.macrodata.load().data
mdata2 = mdata[['realgdp','realcons','realinv']]
names = mdata2.dtype.names
data = mdata2.view((float,3))
data = np.diff(np.log(data), axis=0)
import pandas as pn
df = pn.DataFrame.fromRecords(mdata)
df = np.log(df.reindex(columns=names))
df = (df - df.shift(1)).dropna()
model = VAR(df)
est = model.fit(maxlags=2)
irf = est.irf()
'''
