def __init__(self):
# generate artificial data
np.random.seed(98765678)
nobs = 200
rvs = np.random.randn(nobs,6)
data_exog = rvs
data_exog = sm.add_constant(data_exog, prepend=False)
xbeta = 0.1 + 0.1*rvs.sum(1)
data_endog = np.random.poisson(np.exp(xbeta))
#estimate discretemod.Poisson as benchmark
self.res_discrete = Poisson(data_endog, data_exog).fit(disp=0)
mod_glm = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
self.res_glm = mod_glm.fit()
#estimate generic MLE
self.mod = PoissonGMLE(data_endog, data_exog)
self.res = self.mod.fit(start_params=0.9 * self.res_discrete.params,
method='bfgs', disp=0)
def __init__(self):
# generate artificial data
np.random.seed(98765678)
nobs = 200
rvs = np.random.randn(nobs,6)
data_exog = rvs
data_exog = sm.add_constant(data_exog, prepend=False)
xbeta = 0.1 + 0.1*rvs.sum(1)
data_endog = np.random.poisson(np.exp(xbeta))
#estimate discretemod.Poisson as benchmark
self.res_discrete = Poisson(data_endog, data_exog).fit(disp=0)
mod_glm = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
self.res_glm = mod_glm.fit()
#estimate generic MLE
self.mod = PoissonGMLE(data_endog, data_exog)
self.res = self.mod.fit(start_params=0.9 * self.res_discrete.params,
method='bfgs', disp=0)