def setupClass(cls):
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)
A = np.asarray([[1, 0, 0],['E', 1, 0],['E', 'E', 1]])
B = np.asarray([['E', 0, 0], [0, 'E', 0], [0, 0, 'E']])
results = SVAR(data, svar_type='AB', A=A, B=B).fit(maxlags=3)
cls.res1 = results
cls.res2 = results_svar.SVARdataResults()
def setup_class(cls):
mdata = statsmodels.datasets.macrodata.load_pandas().data
mdata = mdata[['realgdp', 'realcons', 'realinv']]
data = mdata.values
data = np.diff(np.log(data), axis=0)
A = np.asarray([[1, 0, 0], ['E', 1, 0], ['E', 'E', 1]], dtype="U")
B = np.asarray([['E', 0, 0], [0, 'E', 0], [0, 0, 'E']], dtype="U")
results = SVAR(data, svar_type='AB', A=A, B=B).fit(maxlags=3)
cls.res1 = results
#cls.res2 = results_svar.SVARdataResults()
from .results import results_svar_st
cls.res2 = results_svar_st.results_svar1_small
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.tsa.vector_ar.svar_model import SVAR
mdatagen = sm.datasets.macrodata.load(as_pandas=False).data
mdata = mdatagen[['realgdp', 'realcons', 'realinv']]
names = mdata.dtype.names
start = pd.datetime(1959, 3, 31)
end = pd.datetime(2009, 9, 30)
#qtr = pd.DatetimeIndex(start=start, end=end, freq=pd.datetools.BQuarterEnd())
qtr = pd.date_range(start=start, end=end, freq='BQ-MAR')
data = pd.DataFrame(mdata, index=qtr)
data = (np.log(data)).diff().dropna()
#define structural inputs
A = np.asarray([[1, 0, 0], ['E', 1, 0], ['E', 'E', 1]])
B = np.asarray([['E', 0, 0], [0, 'E', 0], [0, 0, 'E']])
A_guess = np.asarray([0.5, 0.25, -0.38])
B_guess = np.asarray([0.5, 0.1, 0.05])
mymodel = SVAR(data, svar_type='AB', A=A, B=B, freq='Q')
res = mymodel.fit(maxlags=3, maxiter=10000, maxfun=10000, solver='bfgs')
res.irf(periods=30).plot(impulse='realgdp',
plot_stderr=True,
stderr_type='mc',
repl=100)
import numpy as np
import statsmodels.api as sm
import pandas as pd
from statsmodels.tsa.vector_ar.svar_model import SVAR
mdatagen = sm.datasets.macrodata.load(as_pandas=False).data
mdata = mdatagen[['realgdp','realcons','realinv']]
names = mdata.dtype.names
start = pd.datetime(1959, 3, 31)
end = pd.datetime(2009, 9, 30)
#qtr = pd.DatetimeIndex(start=start, end=end, freq=pd.datetools.BQuarterEnd())
qtr = pd.DatetimeIndex(start=start, end=end, freq='BQ-MAR')
data = pd.DataFrame(mdata, index=qtr)
data = (np.log(data)).diff().dropna()
#define structural inputs
A = np.asarray([[1, 0, 0],['E', 1, 0],['E', 'E', 1]])
B = np.asarray([['E', 0, 0], [0, 'E', 0], [0, 0, 'E']])
A_guess = np.asarray([0.5, 0.25, -0.38])
B_guess = np.asarray([0.5, 0.1, 0.05])
mymodel = SVAR(data, svar_type='AB', A=A, B=B, freq='Q')
res = mymodel.fit(maxlags=3, maxiter=10000, maxfun=10000, solver='bfgs')
res.irf(periods=30).plot(impulse='realgdp', plot_stderr=True,
stderr_type='mc', repl=100)
