def get_logdet(m):
from gwstatsmodels.tools.compatibility import np_slogdet
logdet = np_slogdet(m)
if logdet[0] == -1: # pragma: no cover
raise ValueError("Matrix is not positive definite")
elif logdet[0] == 0: # pragma: no cover
raise ValueError("Matrix is singular")
else:
logdet = logdet[1]
return logdet
def get_logdet(m):
from gwstatsmodels.tools.compatibility import np_slogdet
logdet = np_slogdet(m)
if logdet[0] == -1: # pragma: no cover
raise ValueError("Matrix is not positive definite")
elif logdet[0] == 0: # pragma: no cover
raise ValueError("Matrix is singular")
else:
logdet = logdet[1]
return logdet
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