import numpy as np
import scikits.statsmodels.api as sm
from scikits.statsmodels.tsa.api import VAR
# some example data
mdata = sm.datasets.macrodata.load().data
mdata = mdata[['realgdp','realcons','realinv']]
names = mdata.dtype.names
data = mdata.view((float,3))
use_growthrate = False #True #False
if use_growthrate:
data = 100 * 4 * np.diff(np.log(data), axis=0)
model = VAR(data, names=names)
res = model.fit(4)
nobs_all = data.shape[0]
#in-sample 1-step ahead forecasts
fc_in = np.array([np.squeeze(res.forecast(model.y[t-20:t], 1))
for t in range(nobs_all-6,nobs_all)])
print fc_in - res.fittedvalues[-6:]
#out-of-sample 1-step ahead forecasts
fc_out = np.array([np.squeeze(VAR(data[:t]).fit(2).forecast(data[t-20:t], 1))
for t in range(nobs_all-6,nobs_all)])
print fc_out - data[nobs_all-6:nobs_all]
import numpy as np
from scikits.statsmodels.tsa.api import VAR
from scikits.statsmodels.api import datasets as ds
mdata = ds.macrodata.load().data[['realgdp', 'realcons', 'realinv']]
names = mdata.dtype.names
data = mdata.view((float,3))
data = np.diff(np.log(data), axis=0)
model = VAR(data, names=names)
est = model.fit(maxlags=2)
def plot_input():
est.plot()
def plot_acorr():
est.plot_acorr()
def plot_irf():
est.irf().plot()
def plot_irf_cum():
irf = est.irf()
irf.plot_cum_effects()
def plot_forecast():
est.plot_forecast(10)
def plot_fevd():
est.fevd(20).plot()
import numpy as np
import scikits.statsmodels.api as sm
from scikits.statsmodels.tsa.api import VAR
# some example data
mdata = sm.datasets.macrodata.load().data
mdata = mdata[['realgdp','realcons','realinv']]
names = mdata.dtype.names
data = mdata.view((float,3))
data = np.diff(np.log(data), axis=0)
model = VAR(data, names=names)
res = model.fit(2)
nobs_all = data.shape[0]
#in-sample 1-step ahead forecasts
fc_in = np.array([np.squeeze(res.forecast(model.y[t-20:t], 1))
for t in range(nobs_all-6,nobs_all)])
print fc_in - res.fittedvalues[-6:]
#out-of-sample 1-step ahead forecasts
fc_out = np.array([np.squeeze(VAR(data[:t]).fit(2).forecast(data[t-20:t], 1))
for t in range(nobs_all-6,nobs_all)])
print fc_out - data[nobs_all-6:nobs_all]
print fc_out - res.fittedvalues[-6:]
