def test_plot_quarter():
dta = sm.datasets.macrodata.load_pandas().data
dates = lmap('Q'.join, zip(dta.year.astype(int).apply(str),
dta.quarter.astype(int).apply(str)))
# test dates argument
quarter_plot(dta.unemp.values, dates)
plt.close('all')
# test with a DatetimeIndex with no freq
parser = pd.tseries.tools.parse_time_string
dta.set_index(pd.DatetimeIndex((x[0] for x in map(parser, dates))),
inplace=True)
quarter_plot(dta.unemp)
plt.close('all')
# w freq
# see pandas #6631
dta.index = pd.DatetimeIndex((x[0] for x in map(parser, dates)),
freq='QS-Oct')
quarter_plot(dta.unemp)
plt.close('all')
# w PeriodIndex
dta.index = pd.PeriodIndex((x[0] for x in map(parser, dates)),
freq='Q')
quarter_plot(dta.unemp)
plt.close('all')
def test_plot_quarter():
dta = sm.datasets.macrodata.load_pandas().data
dates = lmap(
'Q'.join,
zip(
dta.year.astype(int).apply(str),
dta.quarter.astype(int).apply(str)))
# test dates argument
quarter_plot(dta.unemp.values, dates)
plt.close('all')
# test with a DatetimeIndex with no freq
parser = pd.datetools.parse_time_string
dta.set_index(pd.DatetimeIndex((x[0] for x in map(parser, dates))),
inplace=True)
quarter_plot(dta.unemp)
plt.close('all')
# w freq
# see pandas #6631
dta.index = pd.DatetimeIndex((x[0] for x in map(parser, dates)),
freq='QS-Oct')
quarter_plot(dta.unemp)
plt.close('all')
# w PeriodIndex
dta.index = pd.PeriodIndex((x[0] for x in map(parser, dates)), freq='Q')
quarter_plot(dta.unemp)
plt.close('all')
import numpy as np
_quarter_to_day = {
"1": (3, 31),
"2": (6, 30),
"3": (9, 30),
"4": (12, 31),
"I": (3, 31),
"II": (6, 30),
"III": (9, 30),
"IV": (12, 31)
}
_mdays = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_months_with_days = lzip(lrange(1, 13), _mdays)
_month_to_day = dict(zip(map(str, lrange(1, 13)), _months_with_days))
_month_to_day.update(
dict(
zip([
"I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX", "X", "XI",
"XII"
], _months_with_days)))
# regex patterns
_y_pattern = r'^\d?\d?\d?\d$'
_q_pattern = r'''
^ # beginning of string
\d?\d?\d?\d # match any number 1-9999, includes leading zeros
(:?q) # use q or a : as a separator
_quarter_to_day = {
"1" : (3, 31),
"2" : (6, 30),
"3" : (9, 30),
"4" : (12, 31),
"I" : (3, 31),
"II" : (6, 30),
"III" : (9, 30),
"IV" : (12, 31)
}
_mdays = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_months_with_days = lzip(lrange(1,13), _mdays)
_month_to_day = dict(zip(map(str,lrange(1,13)), _months_with_days))
_month_to_day.update(dict(zip(["I", "II", "III", "IV", "V", "VI",
"VII", "VIII", "IX", "X", "XI", "XII"],
_months_with_days)))
# regex patterns
_y_pattern = '^\d?\d?\d?\d$'
_q_pattern = '''
^ # beginning of string
\d?\d?\d?\d # match any number 1-9999, includes leading zeros
(:?q) # use q or a : as a separator
([1-4]|(I{1,3}V?)) # match 1-4 or I-IV roman numerals
x = (1e-4+np.random.randn(nobs)).cumsum()
mcres[ii] = unitroot_adf(x, 2,trendorder=0, autolag=None)[0]
print((mcres<-2.57).sum())
print(np.histogram(mcres))
mcressort = np.sort(mcres)
for ratio in [0.01, 0.025, 0.05, 0.1]:
print(ratio, mcressort[int(nrepl*ratio)])
print('critical values in Green table 20.5')
print('sample size = 100')
print('with constant')
print('0.01: -19.8, 0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47')
print('0.01: -3.50, 0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26')
crvdg = dict([map(float,s.split(':')) for s in ('0.01: -19.8, 0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47'.split(','))])
crvd = dict([map(float,s.split(':')) for s in ('0.01: -3.50, 0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26'.split(','))])
'''
>>> crvd
{0.050000000000000003: -13.699999999999999, 0.97499999999999998: 0.46999999999999997, 0.025000000000000001: -16.300000000000001, 0.01: -11.0}
>>> sorted(crvd.values())
[-16.300000000000001, -13.699999999999999, -11.0, 0.46999999999999997]
'''
#for trend = 0
crit_5lags0p05 =-4.41519 + (-14.0406)/nobs + (-12.575)/nobs**2
print(crit_5lags0p05)
adfstat, _,_,resstore = unitroot_adf(x, 2,trendorder=0, autolag=None, store=1)
x = (1e-4+np.random.randn(nobs)).cumsum()
mcres[ii] = unitroot_adf(x, 2,trendorder=0, autolag=None)[0]
print((mcres<-2.57).sum())
print(np.histogram(mcres))
mcressort = np.sort(mcres)
for ratio in [0.01, 0.025, 0.05, 0.1]:
print(ratio, mcressort[int(nrepl*ratio)])
print('critical values in Green table 20.5')
print('sample size = 100')
print('with constant')
print('0.01: -19.8, 0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47')
print('0.01: -3.50, 0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26')
crvdg = dict([map(float,s.split(':')) for s in ('0.01: -19.8, 0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47'.split(','))])
crvd = dict([map(float,s.split(':')) for s in ('0.01: -3.50, 0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26'.split(','))])
'''
>>> crvd
{0.050000000000000003: -13.699999999999999, 0.97499999999999998: 0.46999999999999997, 0.025000000000000001: -16.300000000000001, 0.01: -11.0}
>>> sorted(crvd.values())
[-16.300000000000001, -13.699999999999999, -11.0, 0.46999999999999997]
'''
#for trend = 0
crit_5lags0p05 =-4.41519 + (-14.0406)/nobs + (-12.575)/nobs**2
print(crit_5lags0p05)
adfstat, _,_,resstore = unitroot_adf(x, 2,trendorder=0, autolag=None, store=1)