import pandas
import matplotlib.pyplot as plt

import statsmodels.api as sm

dta = sm.datasets.macrodata.load_pandas().data

index = pandas.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))
print index

dta.index = index
del dta['year']
del dta['quarter']

print sm.datasets.macrodata.NOTE

print dta.head(10)

fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
dta.realgdp.plot(ax=ax);
legend = ax.legend(loc = 'upper left');
legend.prop.set_size(20);

gdp_cycle, gdp_trend = sm.tsa.filters.hpfilter(dta.realgdp)

gdp_decomp = dta[['realgdp']]
gdp_decomp["cycle"] = gdp_cycle
gdp_decomp["trend"] = gdp_trend

fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
gdp_decomp[["realgdp", "trend"]]["2000-03-31":].plot(ax=ax, fontsize=16);
legend = ax.get_legend()
legend.prop.set_size(20);

bk_cycles = sm.tsa.filters.bkfilter(dta[["infl","unemp"]])
* We lose K observations on both ends. It is suggested to use K=12 for quarterly data.
fig = plt.figure(figsize=(14,10))
ax = fig.add_subplot(111)
bk_cycles.plot(ax=ax, style=['r--', 'b-']);
The CF filter is appropriate for series that may follow a random walk.
print sm.tsa.stattools.adfuller(dta['unemp'])[:3]

print sm.tsa.stattools.adfuller(dta['infl'])[:3]

cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[["infl","unemp"]])
print cf_cycles.head(10)

fig = plt.figure(figsize=(14,10))
ax = fig.add_subplot(111)
cf_cycles.plot(ax=ax, style=['r--','b-']);