def setup_class(cls):
#example 3
nobs = 300
nobs_i = 5
n_groups = nobs // nobs_i
k_vars = 3
from statsmodels.sandbox.panel.random_panel import PanelSample
dgp = PanelSample(nobs, k_vars, n_groups)
dgp.group_means = 2 + np.random.randn(n_groups) #add random intercept
print('seed', dgp.seed)
y = dgp.generate_panel()
x = np.column_stack(
(dgp.exog[:, 1:], dgp.groups[:, None] == np.arange(n_groups)))
cls.dgp = dgp
cls.endog = y
cls.exog = x
cls.res_ols = OLS(y, x).fit()
def setup_class(cls):
#example 3
nobs = 300
nobs_i = 5
n_groups = nobs // nobs_i
k_vars = 3
from statsmodels.sandbox.panel.random_panel import PanelSample
dgp = PanelSample(nobs, k_vars, n_groups)
dgp.group_means = 2 + np.random.randn(n_groups) #add random intercept
print('seed', dgp.seed)
y = dgp.generate_panel()
x = np.column_stack((dgp.exog[:,1:],
dgp.groups[:,None] == np.arange(n_groups)))
cls.dgp = dgp
cls.endog = y
cls.exog = x
cls.res_ols = OLS(y, x).fit()
plt.plot(res_ols.fittedvalues, '-', label='ols')
plt.plot(res.fittedvalues, '-', label='theil')
plt.legend()
plt.title('Polynomial fitting: fitted values')
#plt.show()
if 3 in examples:
#example 3
nobs = 600
nobs_i = 20
n_groups = nobs // nobs_i
k_vars = 3
from statsmodels.sandbox.panel.random_panel import PanelSample
dgp = PanelSample(nobs, k_vars, n_groups)
dgp.group_means = 2 + np.random.randn(n_groups) #add random intercept
print 'seed', dgp.seed
y = dgp.generate_panel()
X = np.column_stack(
(dgp.exog[:, 1:], dgp.groups[:, None] == np.arange(n_groups)))
res_ols = sm.OLS(y, X).fit()
R = np.c_[np.zeros((n_groups, k_vars - 1)), np.eye(n_groups)]
r = np.zeros(n_groups)
R = np.c_[np.zeros((n_groups - 1, k_vars)),
np.eye(n_groups - 1) - 1. / n_groups * np.ones(
(n_groups - 1, n_groups - 1))]
r = np.zeros(n_groups - 1)
R[:, k_vars - 1] = -1
lambd = 1 #1e-4
mod = TheilGLS(y, X, r_matrix=R, q_matrix=r, sigma_prior=lambd)
plt.plot(res_ols.fittedvalues, '-', label='ols')
plt.plot(res.fittedvalues, '-', label='theil')
plt.legend()
plt.title('Polynomial fitting: fitted values')
#plt.show()
if 3 in examples:
#example 3
nobs = 600
nobs_i = 20
n_groups = nobs // nobs_i
k_vars = 3
from statsmodels.sandbox.panel.random_panel import PanelSample
dgp = PanelSample(nobs, k_vars, n_groups)
dgp.group_means = 2 + np.random.randn(n_groups) #add random intercept
print 'seed', dgp.seed
y = dgp.generate_panel()
X = np.column_stack((dgp.exog[:,1:],
dgp.groups[:,None] == np.arange(n_groups)))
res_ols = sm.OLS(y, X).fit()
R = np.c_[np.zeros((n_groups, k_vars-1)), np.eye(n_groups)]
r = np.zeros(n_groups)
R = np.c_[np.zeros((n_groups-1, k_vars)),
np.eye(n_groups-1)-1./n_groups * np.ones((n_groups-1, n_groups-1))]
r = np.zeros(n_groups-1)
R[:, k_vars-1] = -1
lambd = 1 #1e-4
mod = TheilGLS(y, X, r_matrix=R, q_matrix=r, sigma_prior=lambd)
res = mod.fit()
