result.append(r)
return np.array(result) / len(x)
def _acovs_to_acorrs(acovs):
sd = np.sqrt(np.diag(acovs[0]))
return acovs / np.outer(sd, sd)
if __name__ == '__main__':
import scikits.statsmodels.api as sm
from scikits.statsmodels.tsa.vector_ar.util import parse_lutkepohl_data
import scikits.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()
def get_lutkepohl_data(name='e2'):
lut_data = basepath + '/tsa/vector_ar/data/'
path = lut_data + '%s.dat' % name
return util.parse_lutkepohl_data(path)
def get_lutkepohl_data(name='e2'):
lut_data = basepath + '/tsa/vector_ar/data/'
path = lut_data + '%s.dat' % name
return util.parse_lutkepohl_data(path)
return np.array(result) / len(x)
def _acovs_to_acorrs(acovs):
sd = np.sqrt(np.diag(acovs[0]))
return acovs / np.outer(sd, sd)
if __name__ == '__main__':
import scikits.statsmodels.api as sm
from scikits.statsmodels.tsa.vector_ar.util import parse_lutkepohl_data
import scikits.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()
