def _get_yarr(self, endog):
if data_util._is_structured_ndarray(endog):
endog = data_util.struct_to_ndarray(endog)
endog = np.asarray(endog)
if len(endog) == 1: # never squeeze to a scalar
if endog.ndim == 1:
return endog
elif endog.ndim > 1:
return np.asarray([endog.squeeze()])
return endog.squeeze()
def _get_yarr(self, endog):
if data_util._is_structured_ndarray(endog):
endog = data_util.struct_to_ndarray(endog)
endog = np.asarray(endog)
if len(endog) == 1: # never squeeze to a scalar
if endog.ndim == 1:
return endog
elif endog.ndim > 1:
return np.asarray([endog.squeeze()])
return endog.squeeze()
def setup_class(cls):
cls.p = 2
sdata, dates = get_lutkepohl_data('e1')
data = data_util.struct_to_ndarray(sdata)
adj_data = np.diff(np.log(data), axis=0)
# est = VAR(adj_data, p=2, dates=dates[1:], names=names)
cls.model = VAR(adj_data[:-16], dates=dates[1:-16], freq='BQ-MAR')
cls.res = cls.model.fit(maxlags=cls.p)
cls.irf = cls.res.irf(10)
cls.lut = E1_Results()
def __init__(self):
self.p = 2
sdata, dates = get_lutkepohl_data('e1')
data = data_util.struct_to_ndarray(sdata)
adj_data = np.diff(np.log(data), axis=0)
# est = VAR(adj_data, p=2, dates=dates[1:], names=names)
self.model = VAR(adj_data[:-16], dates=dates[1:-16], freq='Q')
self.res = self.model.fit(maxlags=self.p)
self.irf = self.res.irf(10)
self.lut = E1_Results()
def setup_class(cls):
cls.p = 2
sdata, dates = get_lutkepohl_data('e1')
data = data_util.struct_to_ndarray(sdata)
adj_data = np.diff(np.log(data), axis=0)
# est = VAR(adj_data, p=2, dates=dates[1:], names=names)
cls.model = VAR(adj_data[:-16], dates=dates[1:-16], freq='BQ-MAR')
cls.res = cls.model.fit(maxlags=cls.p)
cls.irf = cls.res.irf(10)
cls.lut = E1_Results()
def __init__(self):
self.p = 2
if not have_pandas():
return
sdata, dates = get_lutkepohl_data("e1")
names = sdata.dtype.names
data = data_util.struct_to_ndarray(sdata)
adj_data = np.diff(np.log(data), axis=0)
# est = VAR(adj_data, p=2, dates=dates[1:], names=names)
self.model = VAR(adj_data[:-16], dates=dates[1:-16], names=names, freq="Q")
self.res = self.model.fit(maxlags=self.p)
self.irf = self.res.irf(10)
self.lut = E1_Results()
def _get_xarr(self, exog):
if data_util._is_structured_ndarray(exog):
exog = data_util.struct_to_ndarray(exog)
return np.asarray(exog)
def _get_yarr(self, endog):
if data_util._is_structured_ndarray(endog):
endog = data_util.struct_to_ndarray(endog)
return np.asarray(endog).squeeze()
def _get_xarr(self, exog):
if data_util._is_structured_ndarray(exog):
exog = data_util.struct_to_ndarray(exog)
return np.asarray(exog)
def _acovs_to_acorrs(acovs):
sd = np.sqrt(np.diag(acovs[0]))
return acovs / np.outer(sd, sd)
if __name__ == '__main__':
import statsmodels.api as sm
from statsmodels.tsa.vector_ar.util import parse_lutkepohl_data
import statsmodels.tools.data as data_util
np.set_printoptions(linewidth=140, precision=5)
sdata, dates = parse_lutkepohl_data('data/%s.dat' % 'e1')
names = sdata.dtype.names
data = data_util.struct_to_ndarray(sdata)
adj_data = np.diff(np.log(data), axis=0)
# est = VAR(adj_data, p=2, dates=dates[1:], names=names)
model = VAR(adj_data[:-16], dates=dates[1:-16], names=names)
# model = VAR(adj_data[:-16], dates=dates[1:-16], names=names)
est = model.fit(maxlags=2)
irf = est.irf()
y = est.y[-2:]
"""
# irf.plot_irf()
# i = 2; j = 1
# cv = irf.cum_effect_cov(orth=True)
# print np.sqrt(cv[:, j * 3 + i, j * 3 + i]) / 1e-2
def _get_yarr(self, endog):
if data_util._is_structured_ndarray(endog):
endog = data_util.struct_to_ndarray(endog)
return np.asarray(endog).squeeze()
