Esempio n. 1
0
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')
Esempio n. 2
0
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')
Esempio n. 3
0
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
Esempio n. 4
0
_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
Esempio n. 5
0
            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)
Esempio n. 6
0
            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)