statsmodels.tsa.filters.cf_filter.cffilter¶

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

Christiano Fitzgerald asymmetric, random walk filter.

Parameters:¶

xarray_like

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

lowfloat

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

highfloat

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

driftbool

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).

Returns:¶

cyclearray_like

The features of x between the periodicities low and high.

trendarray_like

The trend in the data with the cycles removed.

See also

statsmodels.tsa.filters.bk_filter.bkfilter

Baxter-King filter.

statsmodels.tsa.filters.bk_filter.hpfilter

Hodrick-Prescott filter.

statsmodels.tsa.seasonal.seasonal_decompose

Decompose a time series using moving averages.

statsmodels.tsa.seasonal.STL

Season-Trend decomposition using LOESS.

Notes

See the notebook Time Series Filters for an overview.

Examples

>>> import statsmodels.api as sm
>>> import pandas as pd
>>> dta = sm.datasets.macrodata.load_pandas().data
>>> index = pd.DatetimeIndex(start='1959Q1', end='2009Q4', freq='Q')
>>> 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)