statsmodels.tsa.filters.cf_filter.cffilter(X, low=6, high=32, drift=True)[source]

Christiano Fitzgerald asymmetric, random walk filter


X : array-like

1 or 2d array to filter. If 2d, variables are assumed to be in columns.

low : float

Minimum period of oscillations. Features below low periodicity are filtered out. Default is 6 for quarterly data, giving a 1.5 year periodicity.

high : float

Maximum period of oscillations. Features above high periodicity are filtered out. Default is 32 for quarterly data, giving an 8 year periodicity.

drift : bool

Whether or not to remove a trend from the data. The trend is estimated as np.arange(nobs)*(X[-1] - X[0])/(len(X)-1)


cycle : array

The features of X between periodicities given by low and high

trend : array

The trend in the data with the cycles removed.


>>> import statsmodels.api as sm
>>> import pandas as pd
>>> dta = sm.datasets.macrodata.load_pandas().data
>>> dates = sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')
>>> index = pd.DatetimeIndex(dates)
>>> dta.set_index(index, inplace=True)
>>> cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[["infl", "unemp"]])
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> cf_cycles.plot(ax=ax, style=['r--', 'b-'])
>>> plt.show()

(Source code, png, hires.png, pdf)


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