def gradient_momcond(self, params, epsilon=1e-4, method='centered'):
momcond = self.momcond_mean
if method == 'centered':
gradmoms = (approx_fprime1(params, momcond, epsilon=epsilon) +
approx_fprime1(params, momcond, epsilon=-epsilon))/2
else:
gradmoms = approx_fprime1(params, momcond, epsilon=epsilon)
return gradmoms
def grad(self, params=None, **kwds):
if params is None:
params = self.params
kwds.setdefault("epsilon", 1e-4)
from gwstatsmodels.sandbox.regression.numdiff import approx_fprime1
return approx_fprime1(params, self.fun, **kwds)
