def setup_class(cls): cls.rng = RandomState(12345) data = dataset_loader(macrodata) cls.cpi = log(data["cpi"]) cls.realgdp = data["realgdp"] cls.inflation = diff(cls.cpi) cls.inflation_change = diff(cls.inflation)
def setup_class(cls): cls.rng = RandomState(12345) t = 1100 y = np.zeros(t) e = cls.rng.standard_normal(t) y[:2] = e[:2] for i in range(3, t): y[i] = 1.5 * y[i - 1] - 0.8 * y[i - 2] + 0.2 * y[i - 3] + e[i] cls.y = y[100:] cls.x = cls.y.std() * cls.rng.randn(t, 2) cls.x = cls.x[100:] cls.z = cls.y + cls.x.sum(1) cls.cpi = log(dataset_loader(macrodata)["cpi"]) cls.inflation = diff(cls.cpi) cls.inflation_change = diff(cls.inflation)
with pytest.raises(InfeasibleTestException, match="The maximum lag you are"): assert np.isfinite(adf.stat) def test_kpss_buggy_timeseries1(): x = np.asarray([0]) adf = KPSS(x, lags=0) # ValueError: cannot convert float NaN to integer with pytest.raises(InfeasibleTestException, match="A minimum of 2 observations"): assert np.isfinite(adf.stat) kpss_autolag_data = ( (dataset_loader(macrodata)["realgdp"], "c", 9), (dataset_loader(sunspots)["SUNACTIVITY"], "c", 7), (dataset_loader(nile)["volume"], "c", 5), (dataset_loader(randhie)["lncoins"], "ct", 75), (dataset_loader(modechoice)["invt"], "ct", 18), ) @pytest.mark.filterwarnings("ignore::DeprecationWarning") @pytest.mark.parametrize("data,trend,lags", kpss_autolag_data) def test_kpss_data_dependent_lags(data, trend, lags): # real GDP from macrodata data set kpss = KPSS(data, trend=trend) assert_equal(kpss.lags, lags)
def setup_class(cls): cls.rng = RandomState(12345) cls.cpi = log(dataset_loader(macrodata)['cpi']) cls.inflation = diff(cls.cpi)
cv_50 = mackinnoncrit(nobs=50) cv_inf = mackinnoncrit() assert np.all(cv_50 <= cv_inf) def test_adf_short_timeseries(): # GH 262 import numpy as np from arch.unitroot import ADF x = np.asarray([0., 0., 0., 0., 0., 0., 1., 1., 0., 0.]) adf = ADF(x) assert_almost_equal(adf.stat, -2.236, decimal=3) assert adf.lags == 1 kpss_autolag_data = ((dataset_loader(macrodata)['realgdp'], 'c', 9), (dataset_loader(sunspots)['SUNACTIVITY'], 'c', 7), (dataset_loader(nile)['volume'], 'c', 5), (dataset_loader(randhie)['lncoins'], 'ct', 75), (dataset_loader(modechoice)['invt'], 'ct', 18)) @pytest.mark.filterwarnings('ignore::DeprecationWarning') @pytest.mark.parametrize('data,trend,lags', kpss_autolag_data) def test_kpss_data_dependent_lags(data, trend, lags): # real GDP from macrodata data set kpss = KPSS(data, trend=trend) assert_equal(kpss.lags, lags) za_test_result = namedtuple('za_test_result', [
def test_autolag_ols_low_memory_smoke(trend, method): data = dataset_loader(macrodata) realgdp = np.log(data["realgdp"]) _autolag_ols_low_memory(realgdp, maxlag=4, trend=trend, method=method)