# some example data
import numpy as np
import pandas
import statsmodels.api as sm
VAR = sm.tsa.VAR

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

    # prepare the dates index
dates = mdata[['year', 'quarter']].astype(int).astype(str)

# the following from the documentation doesn't work for me
#quarterly = dates["year"] + "Q" + dates["quarter"]
#from statsmodels.tsa.base.datetools import dates_from_str
#quarterly = dates_from_str(quarterly)

# hacking around, this works
q = (mdata[['year']].astype("<S4").values + "Q" +
     mdata[['quarter']].values.astype(str)).astype('<S6').ravel()
from statsmodels.tsa.base.datetools import dates_from_str
quarterly = dates_from_str(q)


mdata = mdata[['realgdp','realcons','realinv']]
mdata.index = pandas.DatetimeIndex(quarterly)
data = np.log(mdata).diff().dropna()

    # make a VAR model
model = VAR(data)

results = model.fit(2)

results.summary()

results.plot()

#.. plot:: plots/var_plot_input.py

results.plot_acorr()

#.. plot:: plots/var_plot_acorr.py

model.select_order(15)

results = model.fit(maxlags=15, ic='aic')

lag_order = results.k_ar
results.forecast(data.values[-lag_order:], 5)

irf = results.irf(10)

irf.plot(orth=False)

#.. plot:: plots/var_plot_irf.py

irf.plot(impulse='realgdp')

irf.plot_cum_effects(orth=False)

#.. plot:: plots/var_plot_irf_cum.py

fevd = results.fevd(5)

fevd.summary()

results.fevd(20).plot()

#.. plot:: plots/var_plot_fevd.py

results.test_causality('realgdp', ['realinv', 'realcons'], kind='f')

data

DynamicVAR = sm.tsa.DynamicVAR
var = DynamicVAR(data, lag_order=2, window_type='expanding')

var.coefs

var.coefs.minor_xs('A').info()

# TODO: I have no idea

dir(var.coefs)
from datetime import datetime
var.coefs.major_axis

f = var.forecast(2)
f

f.head()

var.plot_forecast(2)