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WIP: ENH PanelAR1: add Prais-Winston for 1st obs, same as Stata #301

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WIP: ENH PanelAR1: add Prais-Winston for 1st obs, same as Stata #301

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16 changes: 12 additions & 4 deletions statsmodels/sandbox/regression/ar_panel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,23 +36,31 @@ def __init__(self, endog, exog=None, groups=None):
if not exog is None:
self.exog = exog

self.groups_start = (np.diff(groups)!=0)
self.groups_start = np.concatenate(([True], (np.diff(groups)!=0)))
self.groups_valid = ~self.groups_start

def ar1filter(self, xy, alpha):
#print alpha,
return (xy[1:] - alpha * xy[:-1])[self.groups_valid]
if hasattr(self, 'use_prais'):
#print 'using prais'
transformed = np.zeros_like(xy)
transformed[1:] = (xy[1:] - alpha * xy[:-1])
fac = np.sqrt(1.-min(alpha, 1)**2)
transformed[self.groups_start] = xy[self.groups_start] * fac
return transformed
else:
return (xy[1:] - alpha * xy[:-1])[self.groups_valid[1:]]

def fit_conditional(self, alpha):
y = self.ar1filter(self.endog, alpha)
x = self.ar1filter(self.exog, alpha)
res = OLS(y, x).fit()
return res.ssr #res.llf
return res#-res.llf #res.ssr #res.llf


def fit(self):
alpha0 = 0.1 #startvalue
func = self.fit_conditional
func = lambda alpha: -self.fit_conditional(alpha).llf
fitres = optimize.fmin(func, alpha0)

# fit_conditional only returns ssr for now
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