def _loglike_mle(self, params):
"""
Loglikelihood of AR(p) process using exact maximum likelihood
"""
nobs = self.nobs
X = self.X
endog = self.endog
k_ar = self.k_ar
k_trend = self.k_trend
# reparameterize according to Jones (1980) like in ARMA/Kalman Filter
if self.transparams:
params = self._transparams(params)
# get mean and variance for pre-sample lags
yp = endog[:k_ar].copy()
if k_trend:
c = [params[0]] * k_ar
else:
c = [0]
mup = np.asarray(c / (1 - np.sum(params[k_trend:])))
diffp = yp - mup[:, None]
# get inv(Vp) Hamilton 5.3.7
Vpinv = self._presample_varcov(params)
diffpVpinv = np.dot(np.dot(diffp.T, Vpinv), diffp).item()
ssr = sumofsq(endog[k_ar:].squeeze() - np.dot(X, params))
# concentrating the likelihood means that sigma2 is given by
sigma2 = 1.0 / nobs * (diffpVpinv + ssr)
self.sigma2 = sigma2
logdet = slogdet(Vpinv)[1] # TODO: add check for singularity
loglike = -1 / 2.0 * (nobs * (np.log(2 * np.pi) + np.log(sigma2)) - logdet + diffpVpinv / sigma2 + ssr / sigma2)
return loglike
def sigma2(self):
#TODO: allow for DOF correction if exog is included
model = self.model
if model.method == "cmle": # do DOF correction
return 1. / self.nobs * sumofsq(self.resid)
else: # we need to calculate the ssr for the pre-sample
# see loglike for details
lagstart = self.k_trend #TODO: handle exog
p = self.k_ar
params = self.params
meany = params[0] / (1 - params[lagstart:].sum())
pre_resid = model.endog[:p] - meany
# get presample var-cov
Vpinv = model._presample_varcov(params, lagstart)
diffpVpinv = np.dot(np.dot(pre_resid.T, Vpinv), pre_resid).item()
ssr = sumofsq(self.resid[p:]) # in-sample ssr
return 1 / self.nobs * (diffpVpinv + ssr)
def sigma2(self):
#TODO: allow for DOF correction if exog is included
model = self.model
if model.method == "cmle": # do DOF correction
return 1./self.nobs * sumofsq(self.resid)
else: # we need to calculate the ssr for the pre-sample
# see loglike for details
lagstart = self.k_trend #TODO: handle exog
p = self.k_ar
params = self.params
meany = params[0]/(1-params[lagstart:].sum())
pre_resid = model.endog[:p] - meany
# get presample var-cov
Vpinv = model._presample_varcov(params, lagstart)
diffpVpinv = np.dot(np.dot(pre_resid.T,Vpinv),pre_resid).item()
ssr = sumofsq(self.resid[p:]) # in-sample ssr
return 1/self.nobs * (diffpVpinv+ssr)
def _loglike_css(self, params):
"""
Loglikelihood of AR(p) process using conditional sum of squares
"""
nobs = self.nobs
Y = self.Y
X = self.X
ssr = sumofsq(Y.squeeze() - np.dot(X, params))
sigma2 = ssr / nobs
return -nobs / 2 * (np.log(2 * np.pi) + np.log(sigma2)) - ssr / (2 * sigma2)
def _loglike_css(self, params):
"""
Loglikelihood of AR(p) process using conditional sum of squares
"""
nobs = self.nobs
Y = self.Y
X = self.X
ssr = sumofsq(Y.squeeze() - np.dot(X, params))
sigma2 = ssr / nobs
return (-nobs / 2 * (np.log(2 * np.pi) + np.log(sigma2)) - ssr /
(2 * sigma2))
def _loglike_mle(self, params):
"""
Loglikelihood of AR(p) process using exact maximum likelihood
"""
nobs = self.nobs
Y = self.Y
X = self.X
endog = self.endog
k_ar = self.k_ar
k_trend = self.k_trend
# reparameterize according to Jones (1980) like in ARMA/Kalman Filter
if self.transparams:
params = self._transparams(params)
# get mean and variance for pre-sample lags
yp = endog[:k_ar].copy()
if k_trend:
c = [params[0]] * k_ar
else:
c = [0]
mup = np.asarray(c / (1 - np.sum(params[k_trend:])))
diffp = yp - mup[:, None]
# get inv(Vp) Hamilton 5.3.7
Vpinv = self._presample_varcov(params)
diffpVpinv = np.dot(np.dot(diffp.T, Vpinv), diffp).item()
ssr = sumofsq(endog[k_ar:].squeeze() - np.dot(X, params))
# concentrating the likelihood means that sigma2 is given by
sigma2 = 1. / nobs * (diffpVpinv + ssr)
self.sigma2 = sigma2
logdet = np_slogdet(Vpinv)[1] #TODO: add check for singularity
loglike = -1/2.*(nobs*(np.log(2*np.pi) + np.log(sigma2)) - \
logdet + diffpVpinv/sigma2 + ssr/sigma2)
return loglike
def sigma2(self):
model = self.model
if model.method == "cmle": # do DOF correction
return 1. / self.nobs * sumofsq(self.resid)
else:
return self.model.sigma2
def sigma2(self):
model = self.model
if model.method == "cmle": # do DOF correction
return 1./self.nobs * sumofsq(self.resid)
else:
return self.model.sigma2
def loglike(self, params):
"""
The loglikelihood of an AR(p) process
Parameters
----------
params : array
The fitted parameters of the AR model
Returns
-------
llf : float
The loglikelihood evaluated at `params`
Notes
-----
Contains constant term. If the model is fit by OLS then this returns
the conditonal maximum likelihood.
.. math:: \\frac{\\left(n-p\\right)}{2}\\left(\\log\\left(2\\pi\\right)+\\log\\left(\\sigma^{2}\\right)\\right)-\\frac{1}{\\sigma^{2}}\\sum_{i}\\epsilon_{i}^{2}
If it is fit by MLE then the (exact) unconditional maximum likelihood
is returned.
.. math:: -\\frac{n}{2}log\\left(2\\pi\\right)-\\frac{n}{2}\\log\\left(\\sigma^{2}\\right)+\\frac{1}{2}\\left|V_{p}^{-1}\\right|-\\frac{1}{2\\sigma^{2}}\\left(y_{p}-\\mu_{p}\\right)^{\\prime}V_{p}^{-1}\\left(y_{p}-\\mu_{p}\\right)-\\frac{1}{2\\sigma^{2}}\\sum_{t=p+1}^{n}\\epsilon_{i}^{2}
where
:math:`\\mu_{p}` is a (`p` x 1) vector with each element equal to the
mean of the AR process and :math:`\\sigma^{2}V_{p}` is the (`p` x `p`)
variance-covariance matrix of the first `p` observations.
"""
#TODO: Math is on Hamilton ~pp 124-5
#will need to be amended for inclusion of exogenous variables
nobs = self.nobs
Y = self.Y
X = self.X
if self.method == "cmle":
ssr = sumofsq(Y.squeeze() - np.dot(X, params))
sigma2 = ssr / nobs
return -nobs/2 * (np.log(2*np.pi) + np.log(sigma2)) -\
ssr/(2*sigma2)
endog = self.endog
k_ar = self.k_ar
if isinstance(params, tuple):
# broyden (all optimize.nonlin return a tuple until rewrite commit)
params = np.asarray(params)
# reparameterize according to Jones (1980) like in ARMA/Kalman Filter
if self.transparams:
params = self._transparams(params)
# get mean and variance for pre-sample lags
yp = endog[:k_ar]
lagstart = self.k_trend
exog = self.exog
if exog is not None:
lagstart += exog.shape[1]
# xp = exog[:k_ar]
if self.k_trend == 1 and lagstart == 1:
c = [params[0]] * k_ar # constant-only no exogenous variables
else: #TODO: this isn't right
#NOTE: when handling exog just demean and proceed as usual.
c = np.dot(X[:k_ar, :lagstart], params[:lagstart])
mup = np.asarray(c / (1 - np.sum(params[lagstart:])))
diffp = yp - mup[:, None]
# get inv(Vp) Hamilton 5.3.7
Vpinv = self._presample_varcov(params, lagstart)
diffpVpinv = np.dot(np.dot(diffp.T, Vpinv), diffp).item()
ssr = sumofsq(Y.squeeze() - np.dot(X, params))
# concentrating the likelihood means that sigma2 is given by
sigma2 = 1. / nobs * (diffpVpinv + ssr)
logdet = np_slogdet(Vpinv)[1] #TODO: add check for singularity
loglike = -1/2.*(nobs*(np.log(2*np.pi) + np.log(sigma2)) - \
logdet + diffpVpinv/sigma2 + ssr/sigma2)
return loglike
def loglike(self, params):
"""
The unconditional loglikelihood of an AR(p) process
Notes
-----
Contains constant term.
"""
nobs = self.nobs
avobs = self.avobs
Y = self.Y
X = self.X
endog = self.endog
penalty = self.penalty
laglen = self.laglen
# Try reparamaterization:
# just goes to the edge of the boundary for Newton
# reparameterize to ensure stability -- Hamilton 5.9.1
# if not np.all(params==0):
# params = params/(1+np.abs(params))
if isinstance(params,tuple):
# broyden (all optimize.nonlin return a tuple until rewrite commit)
params = np.asarray(params)
usepenalty = False
# http://en.wikipedia.org/wiki/Autoregressive_model
roots = np.roots(np.r_[1,-params[1:]])
mask = np.abs(roots) >= 1
if np.any(mask) and penalty:
mask = np.r_[False, mask]
# signs = np.sign(params)
# np.putmask(params, mask, .9999)
# params *= signs
usepenalty = True
yp = endog[:laglen]
mup = np.asarray([params[0]/(1-np.sum(params[1:]))]*laglen)
#TODO: the above is only correct for constant-only case
diffp = yp-mup[:,None]
# get inv(Vp) Hamilton 5.3.7
params0 = np.r_[-1, params[1:]]
p = len(params) - 1 #TODO: change to trendorder? and above?
p1 = p+1
Vpinv = np.zeros((p,p))
for i in range(1,p1):
for j in range(1,p1):
if i <= j and j <= p:
part1 = np.sum(params0[:i] * params0[j-i:j])
part2 = np.sum(params0[p1-j:p1+i-j]*params0[p1-i:])
Vpinv[i-1,j-1] = part1 - part2
Vpinv = Vpinv + Vpinv.T - np.diag(Vpinv.diagonal())
# this is correct to here
diffpVpinv = np.dot(np.dot(diffp.T,Vpinv),diffp).item()
ssr = sumofsq(Y.squeeze() -np.dot(X,params))
# concentrating the likelihood means that sigma2 is given by
sigma2 = 1./avobs * (diffpVpinv + ssr)
logdet = np_slogdet(Vpinv)[1] #TODO: add check for singularity
loglike = -1/2.*(nobs*(np.log(2*np.pi) + np.log(sigma2)) - \
logdet + diffpVpinv/sigma2 + ssr/sigma2)
if usepenalty:
# subtract a quadratic penalty since we min the negative of loglike
#NOTE: penalty coefficient should increase with iterations
# this uses a static one of 1e3
print "Penalized!"
loglike -= 1000 *np.sum((mask*params)**2)
return loglike
def loglike(self, params):
"""
The loglikelihood of an AR(p) process
Parameters
----------
params : array
The fitted parameters of the AR model
Returns
-------
llf : float
The loglikelihood evaluated at `params`
Notes
-----
Contains constant term. If the model is fit by OLS then this returns
the conditonal maximum likelihood.
.. math:: \\frac{\\left(n-p\\right)}{2}\\left(\\log\\left(2\\pi\\right)+\\log\\left(\\sigma^{2}\\right)\\right)-\\frac{1}{\\sigma^{2}}\\sum_{i}\\epsilon_{i}^{2}
If it is fit by MLE then the (exact) unconditional maximum likelihood
is returned.
.. math:: -\\frac{n}{2}log\\left(2\\pi\\right)-\\frac{n}{2}\\log\\left(\\sigma^{2}\\right)+\\frac{1}{2}\\left|V_{p}^{-1}\\right|-\\frac{1}{2\\sigma^{2}}\\left(y_{p}-\\mu_{p}\\right)^{\\prime}V_{p}^{-1}\\left(y_{p}-\\mu_{p}\\right)-\\frac{1}{2\\sigma^{2}}\\sum_{t=p+1}^{n}\\epsilon_{i}^{2}
where
:math:`\\mu_{p}` is a (`p` x 1) vector with each element equal to the
mean of the AR process and :math:`\\sigma^{2}V_{p}` is the (`p` x `p`)
variance-covariance matrix of the first `p` observations.
"""
#TODO: Math is on Hamilton ~pp 124-5
#will need to be amended for inclusion of exogenous variables
nobs = self.nobs
Y = self.Y
X = self.X
if self.method == "cmle":
ssr = sumofsq(Y.squeeze()-np.dot(X,params))
sigma2 = ssr/nobs
return -nobs/2 * (np.log(2*np.pi) + np.log(sigma2)) -\
ssr/(2*sigma2)
endog = self.endog
k_ar = self.k_ar
if isinstance(params,tuple):
# broyden (all optimize.nonlin return a tuple until rewrite commit)
params = np.asarray(params)
# reparameterize according to Jones (1980) like in ARMA/Kalman Filter
if self.transparams:
params = self._transparams(params)
# get mean and variance for pre-sample lags
yp = endog[:k_ar]
lagstart = self.k_trend
exog = self.exog
if exog is not None:
lagstart += exog.shape[1]
# xp = exog[:k_ar]
if self.k_trend == 1 and lagstart == 1:
c = [params[0]] * k_ar # constant-only no exogenous variables
else: #TODO: this isn't right
#NOTE: when handling exog just demean and proceed as usual.
c = np.dot(X[:k_ar, :lagstart], params[:lagstart])
mup = np.asarray(c/(1-np.sum(params[lagstart:])))
diffp = yp-mup[:,None]
# get inv(Vp) Hamilton 5.3.7
Vpinv = self._presample_varcov(params, lagstart)
diffpVpinv = np.dot(np.dot(diffp.T,Vpinv),diffp).item()
ssr = sumofsq(Y.squeeze() -np.dot(X,params))
# concentrating the likelihood means that sigma2 is given by
sigma2 = 1./nobs * (diffpVpinv + ssr)
logdet = np_slogdet(Vpinv)[1] #TODO: add check for singularity
loglike = -1/2.*(nobs*(np.log(2*np.pi) + np.log(sigma2)) - \
logdet + diffpVpinv/sigma2 + ssr/sigma2)
return loglike
