def setupClass(cls):
data = sm.datasets.sunspots.load()
endog = data.endog
results = []
for lag in range(1, 16 + 1):
endog_tmp = endog[16 - lag:]
r = AR(endog_tmp).fit(maxlag=lag)
# See issue #324 for why we're doing these corrections vs. R
# results
k_ar = r.k_ar
k_trend = r.k_trend
log_sigma2 = np.log(r.sigma2)
#import ipdb; ipdb.set_trace()
aic = r.aic
aic = (aic - log_sigma2) * (1 + k_ar) / (1 + k_ar + k_trend)
aic += log_sigma2
hqic = r.hqic
hqic = (hqic - log_sigma2) * (1 + k_ar) / (1 + k_ar + k_trend)
hqic += log_sigma2
bic = r.bic
bic = (bic - log_sigma2) * (1 + k_ar) / (1 + k_ar + k_trend)
bic += log_sigma2
results.append([aic, hqic, bic, r.fpe])
res1 = np.asarray(results).T.reshape(4, -1, order='C')
# aic correction to match R
cls.res1 = res1
cls.res2 = results_ar.ARLagResults("const").ic
def setupClass(cls):
data = sm.datasets.sunspots.load()
endog = data.endog
results = []
for lag in range(1, 16 + 1):
endog_tmp = endog[16 - lag:]
r = AR(endog_tmp).fit(maxlag=lag)
results.append([r.aic, r.hqic, r.bic, r.fpe])
cls.res1 = np.asarray(results).T.reshape(4, -1, order='C')
cls.res2 = results_ar.ARLagResults("const").ic