def bse(self):
params = self.params
if not fast_kalman or self.model.method == "css":
if len(params) == 1: # can't take an inverse
return np.sqrt(-1.0 / approx_hess_cs(params, self.model.loglike, epsilon=1e-5))
return np.sqrt(np.diag(-inv(approx_hess_cs(params, self.model.loglike, epsilon=1e-5))))
else:
if len(params) == 1:
return np.sqrt(-1.0 / approx_hess(params, self.model.loglike, epsilon=1e-3)[0])
return np.sqrt(np.diag(-inv(approx_hess(params, self.model.loglike, epsilon=1e-3)[0])))
def bse(self):
params = self.params
if not fast_kalman or self.model.method == "css":
if len(params) == 1: # can't take an inverse
return np.sqrt(-1./approx_hess_cs(params,
self.model.loglike, epsilon=1e-5))
return np.sqrt(np.diag(-inv(approx_hess_cs(params,
self.model.loglike, epsilon=1e-5))))
else:
if len(params) == 1:
return np.sqrt(-1./approx_hess(params,
self.model.loglike, epsilon=1e-3)[0])
return np.sqrt(np.diag(-inv(approx_hess(params,
self.model.loglike, epsilon=1e-3)[0])))
def cov_params(self): # add scale argument?
func = self.model.loglike
x0 = self.params
if not fast_kalman or self.model.method == "css":
return -inv(approx_hess_cs(x0, func))
else:
return -inv(approx_hess(x0, func, epsilon=1e-3)[0])
def cov_params(self): # add scale argument?
func = self.model.loglike
x0 = self.params
if not fast_kalman or self.model.method == "css":
return -inv(approx_hess_cs(x0, func))
else:
return -inv(approx_hess(x0, func, 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, 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]
