/statsmodels/tsa/vector_ar/tests/example_svar.py
Python | 25 lines | 20 code | 3 blank | 2 comment | 0 complexity | 6bf6dd15b2496677f429ae1f892ff6b9 MD5 | raw file
Possible License(s): BSD-3-Clause
- import numpy as np
- import pandas as pd
- import statsmodels.datasets.macrodata
- from statsmodels.tsa.vector_ar.svar_model import SVAR
- mdatagen = statsmodels.datasets.macrodata.load().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)