""" Test SVAR estimation """ from statsmodels.compat.platform import PLATFORM_WIN import numpy as np from numpy.testing import assert_allclose, assert_almost_equal import pytest import statsmodels.datasets.macrodata from statsmodels.tsa.vector_ar.svar_model import SVAR DECIMAL_6 = 6 DECIMAL_5 = 5 DECIMAL_4 = 4 class TestSVAR(object): @classmethod 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 def _reformat(self, x): return x[[1, 4, 7, 2, 5, 8, 3, 6, 9, 0], :].ravel("F") def test_A(self): assert_almost_equal(self.res1.A, self.res2.A, DECIMAL_4) def test_B(self): # see issue #3148, adding np.abs to make solution positive # general case will need positive sqrt of covariance matrix assert_almost_equal(np.abs(self.res1.B), self.res2.B, DECIMAL_4) def test_basic(self): res1 = self.res1 res2 = self.res2 assert_allclose(self._reformat(res1.params), res2.b_var, atol=1e-12) bse_st = np.sqrt(np.diag(res2.V_var)) assert_allclose(self._reformat(res1.bse), bse_st, atol=1e-12) def test_llf_ic(self): res1 = self.res1 res2 = self.res2 assert_allclose(res1.llf, res2.ll_var, atol=1e-12) # different definition, missing constant term ? corr_const = -8.51363119922803 assert_allclose(res1.fpe, res2.fpe_var, atol=1e-12) assert_allclose(res1.aic - corr_const, res2.aic_var, atol=1e-12) assert_allclose(res1.bic - corr_const, res2.sbic_var, atol=1e-12) assert_allclose(res1.hqic - corr_const, res2.hqic_var, atol=1e-12) @pytest.mark.smoke def test_irf(self): # mostly SMOKE, API test # this only checks that the methods work and produce the same result res1 = self.res1 errband1 = res1.sirf_errband_mc(orth=False, repl=50, steps=10, signif=0.05, seed=987123, burn=100, cum=False) irf = res1.irf() errband2 = irf.errband_mc(orth=False, svar=True, repl=50, signif=0.05, seed=987123, burn=100) # Windows precision limits require non-zero atol atol = 1e-6 if PLATFORM_WIN else 1e-8 assert_allclose(errband1, errband2, rtol=1e-8, atol=atol)