def parse_lutkepohl_data(path): # pragma: no cover
"""
Parse data files from Lutkepohl (2005) book
Source for data files: www.jmulti.de
"""
from statsmodels.compat.pandas import datetools as dt
from collections import deque
from datetime import datetime
import pandas
import re
regex = re.compile(asbytes('<(.*) (\w)([\d]+)>.*'))
with open(path, 'rb') as f:
lines = deque(f)
to_skip = 0
while asbytes('*/') not in lines.popleft():
#while '*/' not in lines.popleft():
to_skip += 1
while True:
to_skip += 1
line = lines.popleft()
m = regex.match(line)
if m:
year, freq, start_point = m.groups()
break
data = np.genfromtxt(path, names=True, skip_header=to_skip + 1)
n = len(data)
# generate the corresponding date range (using pandas for now)
start_point = int(start_point)
year = int(year)
offsets = {
asbytes('Q'): frequencies.BQuarterEnd(),
asbytes('M'): frequencies.BMonthEnd(),
asbytes('A'): frequencies.BYearEnd()
}
# create an instance
offset = offsets[freq]
inc = offset * (start_point - 1)
start_date = offset.rollforward(datetime(year, 1, 1)) + inc
offset = offsets[freq]
from pandas import DatetimeIndex # pylint: disable=E0611
date_range = DatetimeIndex(start=start_date, freq=offset, periods=n)
return data, date_range
def parse_lutkepohl_data(path): # pragma: no cover
"""
Parse data files from Lütkepohl (2005) book
Source for data files: www.jmulti.de
"""
from collections import deque
from datetime import datetime
import re
regex = re.compile(asbytes(r'<(.*) (\w)([\d]+)>.*'))
with open(path, 'rb') as f:
lines = deque(f)
to_skip = 0
while asbytes('*/') not in lines.popleft():
#while '*/' not in lines.popleft():
to_skip += 1
while True:
to_skip += 1
line = lines.popleft()
m = regex.match(line)
if m:
year, freq, start_point = m.groups()
break
data = (pd.read_csv(path, delimiter=r"\s+",
header=to_skip + 1).to_records(index=False))
n = len(data)
# generate the corresponding date range (using pandas for now)
start_point = int(start_point)
year = int(year)
offsets = {
asbytes('Q'): frequencies.BQuarterEnd(),
asbytes('M'): frequencies.BMonthEnd(),
asbytes('A'): frequencies.BYearEnd()
}
# create an instance
offset = offsets[freq]
inc = offset * (start_point - 1)
start_date = offset.rollforward(datetime(year, 1, 1)) + inc
offset = offsets[freq]
date_range = pd.date_range(start=start_date, freq=offset, periods=n)
return data, date_range
ax.vlines(xs, [0], acorr[:, i])
ax.axhline(0, color='k')
ax.set_ylim([-1, 1])
# hack?
ax.set_xlim([-1, xs[-1] + 1])
mpl.rcParams['font.size'] = old_size
#Example TSA descriptive
data = sm.datasets.macrodata.load()
mdata = data.data
df = DataFrame.from_records(mdata)
quarter_end = frequencies.BQuarterEnd()
df.index = [quarter_end.rollforward(datetime(int(y), int(q) * 3, 1))
for y, q in zip(df.pop('year'), df.pop('quarter'))]
logged = np.log(df.ix[:, ['m1', 'realgdp', 'cpi']])
logged.plot(subplots=True)
log_difference = logged.diff().dropna()
plot_acf_multiple(log_difference.values)
#Example TSA VAR
model = tsa.VAR(log_difference, freq='D')
print(model.select_order())
res = model.fit(2)
print(res.summary())
