def bse(self): # allow user to specify?
if self.model.method == "cmle": # uses different scale/sigma definition
resid = self.resid
ssr = np.dot(resid,resid)
ols_scale = ssr/(self.nobs - self.k_ar - self.k_trend)
return np.sqrt(np.diag(self.cov_params(scale=ols_scale)))
else:
hess = approx_hess(self.params, self.model.loglike)
return np.sqrt(np.diag(-np.linalg.inv(hess[0])))
def hessian(self, params):
"""
Compute the Hessian at params,
Notes
-----
This is a numerical approximation.
"""
loglike = self.loglike
#if self.transparams:
# params = self._invtransparams(params)
if not fast_kalman or self.method == "css":
return approx_hess_cs(params, loglike, epsilon=1e-5)
else:
return approx_hess(params, self.loglike, epsilon=1e-3)[0]
def hessian(self, params):
"""
Compute the Hessian at params,
Notes
-----
This is a numerical approximation.
"""
loglike = self.loglike
#if self.transparams:
# params = self._invtransparams(params)
if not fast_kalman or self.method == "css":
return approx_hess_cs(params, loglike, epsilon=1e-5)
else:
return approx_hess(params, self.loglike, epsilon=1e-3)[0]
def hessian(self, AB_mask):
"""
Returns numerical hessian.
"""
loglike = self.loglike
return approx_hess(AB_mask, loglike)[0]
def hessian(self, AB_mask):
"""
Returns numerical hessian.
"""
loglike = self.loglike
return approx_hess(AB_mask, loglike)[0]
def hessian(self, params):
"""
Returns numerical hessian for now.
"""
loglike = self.loglike
return approx_hess(params, loglike)[0]
